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Institut
- Psychologie (64)
- Fachbereich 4 (50)
- Raum- und Umweltwissenschaften (45)
- Mathematik (44)
- Wirtschaftswissenschaften (26)
- Fachbereich 1 (19)
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- Fachbereich 6 (7)
Even though in most cases time is a good metric to measure costs of algorithms, there are cases where theoretical worst-case time and experimental running time do not match. Since modern CPUs feature an innate memory hierarchy, the location of data is another factor to consider. When most operations of an algorithm are executed on data which is already in the CPU cache, the running time is significantly faster than algorithms where most operations have to load the data from the memory. The topic of this thesis is a new metric to measure costs of algorithms called memory distance—which can be seen as an abstraction of the just mentioned aspect. We will show that there are simple algorithms which show a discrepancy between measured running time and theoretical time but not between measured time and memory distance. Moreover we will show that in some cases it is sufficient to optimize the input of an algorithm with regard to memory distance (while treating the algorithm as a black box) to improve running times. Further we show the relation between worst-case time, memory distance and space and sketch how to define "the usual" memory distance complexity classes.
The present dissertation was developed to emphasize the importance of self-regulatory abilities and to derive novel opportunities to empower self-regulation. From the perspective of PSI (Personality Systems Interactions) theory (Kuhl, 2001), interindividual differences in self-regulation (action vs. state orientation) and their underlying mechanisms are examined in detail. Based on these insights, target-oriented interventions are derived, developed, and scientifically evaluated. The present work comprises a total of four studies which, on the one hand, highlight the advantages of a good self-regulation (e.g., enacting difficult intentions under demands; relation with prosocial power motive enactment and well-being). On the other hand, mental contrasting (Oettingen et al., 2001), an established self-regulation method, is examined from a PSI perspective and evaluated as a method to support individuals that struggle with self-regulatory deficits. Further, derived from PSI theory`s assumptions, I developed and evaluated a novel method (affective shifting) that aims to support individuals in overcoming self-regulatory deficits. Thereby affective shifting supports the decisive changes in positive affect for successful intention enactment (Baumann & Scheffer, 2010). The results of the present dissertation show that self-regulated changes between high and low positive affect are crucial for efficient intention enactment and that methods such as mental contrasting and affective shifting can empower self-regulation to support individuals to successfully close the gap between intention and action.
Climate fluctuations and the pyroclastic depositions from volcanic activity both influence ecosystem functioning and biogeochemical cycling in terrestrial and marine environments globally. These controlling factors are crucial for the evolution and fate of the pristine but fragile fjord ecosystem in the Magellanic moorlands (~53°S) of southernmost Patagonia, which is considered a critical hotspot for organic carbon burial and marine bioproductivity. At this active continental margin in the core zone of the southern westerly wind belt (SWW), frequent Plinian eruptions and the extremely variable, hyper-humid climate should have efficiently shaped ecosystem functioning and land-to-fjord mass transfer throughout the Late Holocene. However, a better understanding of the complex process network defining the biogeochemical cycling at this land-to-fjord continuum principally requires a detailed knowledge of substrate weathering and pedogenesis in the context of the extreme climate. Yet, research on soils, the ubiquitous presence of tephra and the associated chemical weathering, secondary mineral (trans)formation and organic matter (OM) turnover processes is rare in this remote region. This complicates an accurate reconstruction of the ecosystem´s potentially sensitive response to past environmental impacts, including the dynamics of Late Holocene land-to-fjord fluxes as a function of volcanic activity and strong hydroclimate variability.
Against this background, this PhD thesis aims to disentangle the controlling factors that modulate the terrigenous element mobilization and export mechanisms in the hyper-humid Patagonian Andes and assesses their significance for fjord primary productivity over the past 4.5 kyrs BP. For the first time, distinct biogeochemical characteristics of the regional weathering system serve as major criterion in paleoenvironmental reconstruction in the area. This approach includes broad-scale mineralogical and geochemical analyses of basement lithologies, four soil profiles, volcanic ash deposits, the non-karst stalagmite MA1 and two lacustrine sediment cores. In order to pay special attention to the possibly important temporal variations of pedosphere-atmosphere interaction and ecological consequences initiated by volcanic eruptions, the novel data were evaluated together with previously published reconstructions of paleoclimate and paleoenvironmental conditions.
The devastative high-tephra loading of a single eruption from Mt. Burney volcano (MB2 at 4.216 kyrs BP) sustainably transformed this vulnerable fjord ecosystem, while acidic peaty Andosols developed from ~2.5 kyrs BP onwards after the recovery from millennium-scale acidification. The special setting is dominated by most variable redox-pH conditions, profound volcanic ash weathering and intense OM turnover processes, which are closely linked and ultimately regulated by SWW-induced water-level fluctuations. Constant nutrient supply though sea spray deposition represents a further important control on peat accumulation and OM turnover dynamics. These extreme environmental conditions constrain the biogeochemical framework for an extended land-to-fjord export of leachates comprising various organic and inorganic colloids (i.e., Al-humus complexes and Fe-(hydr)oxides). Such tephra- and/or Andosol-sourced flux contains high proportions of terrigenous organic carbon (OCterr) and mobilized essential (micro)nutrients, e.g., bio-available Fe, that are beneficial for fjord bioproductivity. It can be assumed that this supply of bio-available Fe produced by specific Fe-(hydr)oxide (trans)formation processes from tephra components may outlast more than 6 kyrs and surpasses the contribution from basement rock weathering and glacial meltwaters. However, the land-to-fjord exports of OCterr and bio-available Fe occur mostly asynchronous and are determined by the frequency and duration of redox cycles in soils or are initiated by SWW-induced extreme weather events.
The verification of (crypto)tephra layers embedded stalagmite MA1 enabled the accurate dating of three smaller Late Holocene eruptions from Mt. Burney (MB3 at 2.291 kyrs BP and MB4 at 0.853 kyrs BP) and Aguilera (A1 at 2.978 kyrs BP) volcanoes. Irrespective of the improvement of the regional tephrochronology, the obtained precise 230Th/U-ages allowed constraints on the ecological consequences caused by these Plinian eruptions. The deposition of these thin tephra layers should have entailed a very beneficial short-term stimulation of fjord bioproductivity with bio-available Fe and other (micro)nutrients, which affected the entire area between 52°S and 53°S 30´, respectively. For such beneficial effects, the thickness of tephra deposited to this highly vulnerable peatland ecosystem should be below a threshold of 1 cm.
The Late Holocene element mobilization and land-to-fjord transport was mainly controlled by (i) volcanic activity and tephra thickness, (ii) SWW-induced and southern hemispheric climate variability and (iii) the current state of the ecosystem. The influence of cascading climate and environmental impacts on OCterr and Fe-(hydr)oxide fluxes to can be categorized by four individual, in part overlapping scenarios. These different scenarios take into account the previously specified fundamental biogeochemical mechanisms and define frequently recurring patterns of ecosystem feedbacks governing the land-to-fjord mass transfer in the hyper-humid Patagonian Andes on the centennial-scale. This PhD thesis provides first evidence for a primarily tephra-sourced, continuous and long-lasting (micro)nutrient fertilization for phytoplankton growth in South Patagonian fjords, which is ultimately modulated by variations in SWW-intensity. It highlights the climate sensitivity of such critical land-to-fjord element transport and particularly emphasizes the important but so far underappreciated significance of volcanic ash inputs for biogeochemical cycles at active continental margins.
Der digitale Fortschritt der vergangenen Jahrzehnte beruht zu einem großen Teil auf der Innovationskraft junger aufstrebender Unternehmen. Während diese Unternehmen auf der einen Seite ihr hohes Maß an Innovativität eint, entsteht für diese zeitgleich auch ein hoher Bedarf an finanziellen Mitteln, um ihre geplanten Innovations- und Wachstumsziele auch in die Tat umsetzen zu können. Da diese Unternehmen häufig nur wenige bis keine Unternehmenswerte, Umsätze oder auch Profitabilität vorweisen können, gestaltet sich die Aufnahme von externem Kapital häufig schwierig bis unmöglich. Aus diesem Umstand entstand in der Mitte des zwanzigsten Jahrhunderts das Geschäftsmodell der Risikofinanzierung, des sogenannten „Venture Capitals“. Dabei investieren Risikokapitalgeber in aussichtsreiche junge Unternehmen, unterstützen diese in ihrem Wachstum und verkaufen nach einer festgelegten Dauer ihre Unternehmensanteile, im Idealfall zu einem Vielfachen ihres ursprünglichen Wertes. Zahlreiche junge Unternehmen bewerben sich um Investitionen dieser Risikokapitalgeber, doch nur eine sehr geringe Zahl erhält diese auch. Um die aussichtsreichsten Unternehmen zu identifizieren, sichten die Investoren die Bewerbungen anhand verschiedener Kriterien, wodurch bereits im ersten Schritt der Bewerbungsphase zahlreiche Unternehmen aus dem Kreis potenzieller Investmentobjekte ausscheiden. Die bisherige Forschung diskutiert, welche Kriterien Investoren zu einer Investition bewegen. Daran anschließend verfolgt diese Dissertation das Ziel, ein tiefergehendes Verständnis darüber zu erlangen, welche Faktoren die Entscheidungsfindung der Investoren beeinflussen. Dabei wird vor allem auch untersucht, wie sich persönliche Faktoren der Investoren, sowie auch der Unternehmensgründer, auf die Investitionsentscheidung auswirken. Ergänzt werden diese Untersuchungen zudem durch die Analyse der Wirkung des digitalen Auftretens von Unternehmensgründern auf die Entscheidungsfindung von Risikokapitalgebern. Des Weiteren verfolgt diese Dissertation als zweites Ziel einen Erkenntnisgewinn über die Auswirkungen einer erfolgreichen Investition auf den Unternehmensgründer. Insgesamt umfasst diese Dissertation vier Studien, die im Folgenden näher beschrieben werden.
In Kapitel 2 wird untersucht, inwiefern sich bestimmte Humankapitaleigenschaften des Investors auf dessen Entscheidungsverhalten auswirken. Mithilfe vorangegangener Interviews und Literaturrecherchen wurden insgesamt sieben Kriterien identifiziert, die Risikokapitalinvestoren in ihrer Entscheidungsfindung nutzen. Daraufhin nahmen 229 Investoren an einem Conjoint Experiment teil, mithilfe dessen gezeigt werden konnte, wie wichtig die jeweiligen Kriterien im Rahmen der Entscheidung sind. Von besonderem Interesse ist dabei, wie sich die Wichtigkeit der Kriterien in Abhängigkeit der Humankapitaleigenschaften der Investoren unterscheiden. Dabei kann gezeigt werden, dass sich die Wichtigkeit der Kriterien je nach Bildungshintergrund und Erfahrung der Investoren unterscheidet. So legen beispielsweise Investoren mit einem höheren Bildungsabschluss und Investoren mit unternehmerischer Erfahrung deutlich mehr Wert auf die internationale Skalierbarkeit der Unternehmen. Zudem unterscheidet sich die Wichtigkeit der Kriterien auch in Abhängigkeit der fachlichen Ausbildung. So legen etwa Investoren mit einer fachlichen Ausbildung in Naturwissenschaften einen deutlich stärkeren Fokus auf den Mehrwert des Produktes beziehungsweise der Dienstleistung. Zudem kann gezeigt werden, dass Investoren mit mehr Investitionserfahrung die Erfahrung des Managementteams wesentlich wichtiger einschätzen als Investoren mit geringerer Investitionserfahrung. Diese Ergebnisse ermöglichen es Unternehmensgründern ihre Bewerbungen um eine Risikokapitalfinanzierung zielgenauer auszurichten, etwa durch eine Analyse des beruflichen Hintergrunds der potentiellen Investoren und eine damit einhergehende Anpassung der Bewerbungsunterlagen, zum Beispiel durch eine stärkere Schwerpunktsetzung besonders relevanter Kriterien.
Die in Kapitel 3 vorgestellte Studie bedient sich der Daten des gleichen Conjoint Experiments aus Kapitel 2, legt hierbei allerdings einen Fokus auf den Unterschied zwischen Investoren aus den USA und Investoren aus Kontinentaleuropa. Dazu wurden Subsamples kreiert, in denen 128 Experimentteilnehmer in den USA angesiedelt sind und 302 in Kontinentaleuropa. Die Analyse der Daten zeigt, dass US-amerikanische Investoren, im Vergleich zu Investoren in Kontinentaleuropa, einen signifikant stärkeren Fokus auf das Umsatzwachstum der Unternehmen legen. Zudem legen kontinentaleuropäische Investoren einen deutlich stärkeren Fokus auf die internationale Skalierbarkeit der Unternehmen. Um die Ergebnisse der Analyse besser interpretieren zu können, wurden diese im Anschluss mit vier amerikanischen und sieben europäischen Investoren diskutiert. Dabei bestätigen die europäischen Investoren die Wichtigkeit der hohen internationalen Skalierbarkeit aufgrund der teilweise geringen Größe europäischer Länder und dem damit zusammenhängenden Zwang, schnell international skalieren zu können, um so zufriedenstellende Wachstumsraten zu erreichen. Des Weiteren wurde der vergleichsweise geringere Fokus auf das Umsatzwachstum in Europa mit fehlenden Mitteln für eine schnelle Expansion begründet. Gleichzeitig wird der starke Fokus der US-amerikanischen Investoren auf Umsatzwachstum mit der höheren Tendenz zu einem Börsengang in den USA begründet, bei dem hohe Umsätze als Werttreiber dienen. Die Ergebnisse dieses Kapitels versetzen Unternehmensgründer in die Lage, ihre Bewerbung stärker an die wichtigsten Kriterien der potenziellen Investoren auszurichten, um so die Wahrscheinlichkeit einer erfolgreichen Investitionsentscheidung zu erhöhen. Des Weiteren bieten die Ergebnisse des Kapitels Investoren, die sich an grenzüberschreitenden syndizierten Investitionen beteiligen, die Möglichkeit, die Präferenzen der anderen Investoren besser zu verstehen und die Investitionskriterien besser auf potenzielle Partner abzustimmen.
Kapitel 4 untersucht ob bestimmte Charaktereigenschaften des sogenannten Schumpeterschen Entrepreneurs einen Einfluss auf die Wahrscheinlichkeit eines zweiten Risikokapitalinvestments haben. Dazu wurden von Gründern auf Twitter gepostete Nachrichten sowie Information von Investitionsrunden genutzt, die auf der Plattform Crunchbase zur Verfügung stehen. Insgesamt wurden mithilfe einer Textanalysesoftware mehr als zwei Millionen Tweets von 3313 Gründern analysiert. Die Ergebnisse der Studie deuten an, dass einige Eigenschaften, die typisch für Schumpetersche Gründer sind, die Chancen für eine weitere Investition erhöhen, während andere keine oder negative Auswirkungen haben. So erhöhen Gründer, die auf Twitter einen starken Optimismus sowie ihre unternehmerische Vision zur Schau stellen die Chancen auf eine zweite Risikokapitalfinanzierung, gleichzeitig werden diese aber durch ein zu starkes Streben nach Erfolg reduziert. Diese Ergebnisse haben eine hohe praktische Relevanz für Unternehmensgründer, die sich auf der Suche nach Risikokapital befinden. Diese können dadurch ihr virtuelles Auftreten („digital identity“) zielgerichteter steuern, um so die Wahrscheinlichkeit einer weiteren Investition zu erhöhen.
Abschließend wird in Kapitel 5 untersucht, wie sich die digitale Identität der Gründer verändert, nachdem diese eine erfolgreiche Risikokapitalinvestition erhalten haben. Dazu wurden sowohl Twitter-Daten als auch Crunchbase-Daten genutzt, die im Rahmen der Erstellung der Studie in Kapitel 4 erhoben wurden. Mithilfe von Textanalyse und Paneldatenregressionen wurden die Tweets von 2094 Gründern vor und nach Erhalt der Investition untersucht. Dabei kann gezeigt werden, dass der Erhalt einer Risikokapitalinvestition das Selbstvertrauen, die positiven Emotionen, die Professionalisierung und die Führungsqualitäten der Gründer erhöhen. Gleichzeitig verringert sich allerdings die Authentizität der von den Gründern verfassten Nachrichten. Durch die Verwendung von Interaktionseffekten kann zudem gezeigt werden, dass die Steigerung des Selbstvertrauens positiv durch die Reputation des Investors moderiert wird, während die Höhe der Investition die Authentizität negativ moderiert. Investoren haben durch diese Erkenntnisse die Möglichkeit, den Weiterentwicklungsprozess der Gründer nach einer erfolgreichen Investition besser nachvollziehen zu können, wodurch sie in die Lage versetzt werden, die Aktivitäten ihrer Gründer auf Social Media Plattformen besser zu kontrollieren und im Bedarfsfall bei ihrer Anpassung zu unterstützen.
Die in den Kapiteln 2 bis 5 vorgestellten Studien dieser Dissertation tragen damit zu einem besseren Verständnis der Entscheidungsfindung im Venture Capital Prozess bei. Der bisherige Stand der Forschung wird um Erkenntnisse erweitert, die sowohl den Einfluss der Eigenschaften der Investoren als auch der Gründer betreffen. Zudem wird auch gezeigt, wie sich die Investition auf den Gründer selbst auswirken kann. Die Implikationen der Ergebnisse, sowie Limitationen und Möglichkeiten künftiger Forschung werden in Kapitel 6 näher beschrieben. Da die in dieser Dissertation verwendeten Methoden und Daten erst seit wenigen Jahren im Kontext der Venture Capital Forschung genutzt werden, beziehungsweise überhaupt verfügbar sind, bietet sie sich als eine Grundlage für weitere Forschung an.
Broadcast media such as television have spread rapidly worldwide in the last century. They provide viewers with access to new information and also represent a source of entertainment that unconsciously exposes them to different social norms and moral values. Although the potential impact of exposure to television content have been studied intensively in economic research in recent years, studies examining the long-term causal effects of media exposure are still rare. Therefore, Chapters 2 to 4 of this thesis contribute to the better understanding of long-term effects of television exposure.
Chapter 2 empirically investigates whether access to reliable environmental information through television can influence individuals' environmental awareness and pro-environmental behavior. Analyzing exogenous variation in Western television reception in the German Democratic Republic shows that access to objective reporting on environmental pollution can enhance concerns regarding pollution and affect the likelihood of being active in environmental interest groups.
Chapter 3 utilizes the same natural experiment and explores the relationship between exposure to foreign mass media content and xenophobia. In contrast to the state television broadcaster in the German Democratic Republic, West German television regularly confronted its viewers with foreign (non-German) broadcasts. By applying multiple measures for xenophobic attitudes, our findings indicate a persistent mitigating impact of foreign media content on xenophobia.
Chapter 4 deals with another unique feature of West German television. In contrast to East German media, Western television programs regularly exposed their audience to unmarried and childless characters. The results suggest that exposure to different gender stereotypes contained in television programs can affect marriage, divorce, and birth rates. However, our findings indicate that mainly women were affected by the exposure to unmarried and childless characters.
Chapter 5 examines the influence of social media marketing on crowd participation in equity crowdfunding. By analyzing 26,883 investment decisions on three German equity crowdfunding platforms, our results show that startups can influence the success of their equity crowdfunding campaign through social media posts on Facebook and Twitter.
In Chapter 6, we incorporate the concept of habit formation into the theoretical literature on trade unions and contribute to a better understanding of how internal habit preferences influence trade union behavior. The results reveal that such internal reference points lead trade unions to raise wages over time, which in turn reduces employment. Conducting a numerical example illustrates that the wage effects and the decline in employment can be substantial.
Statistical matching offers a way to broaden the scope of analysis without increasing respondent burden and costs. These would result from conducting a new survey or adding variables to an existing one. Statistical matching aims at combining two datasets A and B referring to the same target population in order to analyse variables, say Y and Z, together, that initially were not jointly observed. The matching is performed based on matching variables X that correspond to common variables present in both datasets A and B. Furthermore, Y is only observed in B and Z is only observed in A. To overcome the fact that no joint information on X, Y and Z is available, statistical matching procedures have to rely on suitable assumptions. Therefore, to yield a theoretical foundation for statistical matching, most procedures rely on the conditional independence assumption (CIA), i.e. given X, Y is independent of Z.
The goal of this thesis is to encompass both the statistical matching process and the analysis of the matched dataset. More specifically, the aim is to estimate a linear regression model for Z given Y and possibly other covariates in data A. Since the validity of the assumptions underlying the matching process determine the validity of the obtained matched file, the accuracy of statistical inference is determined by the suitability of the assumptions. By putting the focus on these assumptions, this work proposes a systematic categorisation of approaches to statistical matching by relying on graphical representations in form of directed acyclic graphs. These graphs are particularly useful in representing dependencies and independencies which are at the heart of the statistical matching problem. The proposed categorisation distinguishes between (a) joint modelling of the matching and the analysis (integrated approach), and (b) matching subsequently followed by statistical analysis of the matched dataset (classical approach). Whereas the classical approach relies on the CIA, implementations of the integrated approach are only valid if they converge, i.e. if the specified models are identifiable and, in the case of MCMC implementations, if the algorithm converges to a proper distribution.
In this thesis an implementation of the integrated approach is proposed, where the imputation step and the estimation step are jointly modelled through a fully Bayesian MCMC estimation. It is based on a linear regression model for Z given Y and accounts for both a linear regression model and a random effects model for Y. Furthermore, it yields its validity when the instrumental variable assumption (IVA) holds. The IVA corresponds to: (a) Z is independent of a subset X’ of X given Y and X*, where X* = X\X’ and (b) Y is correlated with X’ given X*. The proof, that the joint Bayesian modelling of both the model for Z and the model for Y through an MCMC simulation converges to a proper distribution is provided in this thesis. In a first model-based simulation study, the proposed integrated Bayesian procedure is assessed with regard to the data situation, convergence issues, and underlying assumptions. Special interest lies in the investigation of the interplay of the Y and the Z model within the imputation process. It turns out that failure scenarios can be distinguished by comparing the CIA and the IVA in the completely observed dataset.
Finally, both approaches to statistical matching, i.e. the classical approach and the integrated approach, are subject to an extensive comparison in (1) a model-based simulation study and (2) a simulation study based on the AMELIA dataset, which is an openly available very large synthetic dataset and, by construction, similar to the EU-SILC survey. As an additional integrated approach, a Bayesian additive regression trees (BART) model is considered for modelling Y. These integrated procedures are compared to the classical approach represented by predictive mean matching in the form of multiple imputations by chained equation. Suitably chosen, the first simulation framework offers the possibility to clarify aspects related to the underlying assumptions by comparing the IVA and the CIA and by evaluating the impact of the matching variables. Thus, within this simulation study two related aspects are of special interest: the assumptions underlying each method and the incorporation of additional matching variables. The simulation on the AMELIA dataset offers a close-to-reality framework with the advantage of knowing the whole setting, i.e. the whole data X, Y and Z. Special interest lies in investigating assumptions through adding and excluding auxiliary variables in order to enhance conditional independence and assess the sensitivity of the methods to this issue. Furthermore, the benefit of having an overlap of units in data A and B for which information on X, Y, Z is available is investigated. It turns out that the integrated approach yields better results than the classical approach when the CIA clearly does not hold. Moreover, even when the classical approach obtains unbiased results for the regression coefficient of Y in the model for Z, it is the method relying on BART that over all coefficients performs best.
Concluding, this work constitutes a major contribution to the clarification of assumptions essential to any statistical matching procedure. By introducing graphical models to identify existing approaches to statistical matching combined with the subsequent analysis of the matched dataset, it offers an extensive overview, categorisation and extension of theory and application. Furthermore, in a setting where none of the assumptions are testable (since X, Y and Z are not observed together), the integrated approach is a valuable asset by offering an alternative to the CIA.
Let K be a compact subset of the complex plane. Then the family of polynomials P is dense in A(K), the space of all continuous functions on K that are holomorphic on the interior of K, endowed with the uniform norm, if and only if the complement of K is connected. This is the statement of Mergelyan's celebrated theorem.
There are, however, situations where not all polynomials are required to approximate every f ϵ A(K) but where there are strict subspaces of P that are still dense in A(K). If, for example, K is a singleton, then the subspace of all constant polynomials is dense in A(K). On the other hand, if 0 is an interior point of K, then no strict subspace of P can be dense in A(K).
In between these extreme cases, the situation is much more complicated. It turns out that it is mostly determined by the geometry of K and its location in the complex plane which subspaces of P are dense in A(K). In Chapter 1, we give an overview of the known results.
Our first main theorem, which we will give in Chapter 3, deals with the case where the origin is not an interior point of K. We will show that if K is a compact set with connected complement and if 0 is not an interior point of K, then any subspace Q ⊂ P which contains the constant functions and all but finitely many monomials is dense in A(K).
There is a close connection between lacunary approximation and the theory of universality. At the end of Chapter 3, we will illustrate this connection by applying the above result to prove the existence of certain universal power series. To be specific, if K is a compact set with connected complement, if 0 is a boundary point of K and if A_0(K) denotes the subspace of A(K) of those functions that satisfy f(0) = 0, then there exists an A_0(K)-universal formal power series s, where A_0(K)-universal means that the family of partial sums of s forms a dense subset of A_0(K).
In addition, we will show that no formal power series is simultaneously universal for all such K.
The condition on the subspace Q in the main result of Chapter 3 is quite restrictive, but this should not be too surprising: The result applies to the largest possible class of compact sets.
In Chapter 4, we impose a further restriction on the compact sets under consideration, and this will allow us to weaken the condition on the subspace Q. The result that we are going to give is similar to one of those presented in the first chapter, namely the one due to Anderson. In his article “Müntz-Szasz type approximation and the angular growth of lacunary integral functions”, he gives a criterion for a subspace Q of P to be dense in A(K) where K is entirely contained in some closed sector with vertex at the origin.
We will consider compact sets with connected complement that are -- with the possible exception of the origin -- entirely contained in some open sector with vertex at the origin. What we are going to show is that if K\{0} is contained in an open sector of opening angle 2α and if Λ is some subset of the nonnegative integers, then the span of {z → z^λ : λ ϵ Λ} is dense in A(K) whenever 0 ϵ Λ and some Müntz-type condition is satisfied.
Conversely, we will show that if a similar condition is not satisfied, then we can always find a compact set K with connected complement such that K\{0} is contained in some open sector of opening angle 2α and such that the span of {z → z^λ : λ ϵ Λ} fails to be dense in A(K).
Issues in Price Measurement
(2022)
This thesis focuses on the issues in price measurement and consists of three chapters. Due to outdated weighting information, a Laspeyres-based consumer price index (CPI) is prone to accumulating upward bias. Therefore, chapter 1 introduces and examines simple and transparent revision approaches that retrospectively address the source of the bias. They provide a consistent long-run time series of the CPI and require no additional information. Furthermore, a coherent decomposition of the bias into the contributions of individual product groups is developed. In a case study, the approaches are applied to a Laspeyres-based CPI. The empirical results confirm the theoretical predictions. The proposed revision approaches are adoptable not only to most national CPIs but also to other price-level measures such as the producer price index or the import and export price indices.
Chapter 2 is dedicated to the measurement of import and export price indices. Such indices are complicated by the impact of exchange rates. These indices are usually also compiled by some Laspeyres type index. Therefore, substitution bias is an issue. The terms of trade (ratio of export and import price index) are therefore also likely to be distorted. The underlying substitution bias accumulates over time. The present article applies a simple and transparent retroactive correction approach that addresses the source of the substitution bias and produces meaningful long-run time series of import and export price levels and, therefore, of the terms of trade. Furthermore, an empirical case study is conducted that demonstrates the efficacy and versatility of the correction approach.
Chapter 3 leaves the field of index revision and studies another issue in price measurement, namely, the economic evaluation of digital products in monetary terms that have zero market prices. This chapter explores different methods of economic valuation and pricing of free digital products and proposes an alternative way to calculate the economic value and a shadow price of free digital products: the Usage Cost Model (UCM). The goal of the chapter is, first of all, to formulate a theoretical framework and incorporate an alternative measure of the value of free digital products. However, an empirical application is also made to show the work of the theoretical model. Some conclusions on applicability are drawn at the end of the chapter.
Soils in forest ecosystems bear a high potential as carbon (C) sinks in the mitigation of climate change. The amount and characteristics of soil organic matter (SOM) are driven by inputs, transformation, degradation and stabilization of organic substances. While tree species fuel the C cycle by producing aboveground and belowground litter, soil microorganisms are crucial for litter degradation as well as the formation and stabilization of SOM. Nonetheless, our knowledge about the tree species effect on the SOM status is limited, inconsistent and blurred. The investigation of tree species effects on SOM is challenging because in long-established forest ecosystems the spatial distribution of tree species is a result of the interplay of environmental factors including climate, geomorphology and soil chemistry. Moreover, tree distribution can further vary with forest successional stage and silvicultural management. Since these factors also directly affect the soil C-status, it is difficult to identify a pure “tree species effect” on the SOM status at regular forested sites. It therefore remains unclear in how far tree species-specific litter with different quality influences the microbial driven turnover and formation of SOM.
Tree species effects on SOM and related soil microbial properties were investigated by examining soil profiles (comprising organic forest floor horizons and mineral soil layers) in different forest stands at the recultivated spoil heap ‘Sophienhöhe’ located at the lignite open-cast mine Hambach near Jülich, Germany. The afforested sites comprised monocultural stands of Douglas fir (Pseudotsuga menziesii), black pine (Pinus nigra), European beech (Fagus sylvatica) and red oak (Quercus rubra) as well as a mixed deciduous stand site planted mainly with hornbeam (Carpinus betulus), lime (Tilia cordata) and common oak (Quercus robur) that were grown for 35 years under identical soil and geomorphological conditions. Because the parent material used for site recultivation was free from organic matter or coal material, the SOM accumulation is entirely the result of in situ soil development due to the impact of tree species.
The first study revealed that tree species had a significant effect on soil organic carbon (SOC) stocks, stoichiometric patterns of C, nitrogen (N), sulfur (S), hydrogen (H) and oxygen (O) as well as the microbial biomass carbon (MBC) content in the forest floor and the top mineral soil layers (0-5 cm, 5-10 cm, 10-30 cm). In general, forest floor SOC stocks were significantly higher at coniferous forest stands compared to deciduous tree species, whereas in mineral soil layers the differences were smaller. Thus, the impact of tree species decreased with increasing soil depth. By investigating the linkage of the natural abundance of 13C and 15N in the soil depth gradients with C:N and O:C stoichiometry, the second study showed that differences in SOC stocks and SOM quality resulted from a tree species-dependent turnover of SOM. Significantly higher turnover of organic matter in soils under deciduous tree species depended to 46 % on the quality of litterfall and root inputs (N content, C:N, O:C ratio), and on the initial isotopic signatures of litterfall. Hence, SOM composition and turnover also depends on additional – presumably microbially driven – factors. The subsequent results of the third study revealed that differences in SOM composition and related soil microbial properties were linked to different microbial communities. Phospholipid fatty acid (PLFA) patterns in the soil profiles indicated that the supply and availability of C and nutrient-rich substrates drive the distribution of fungi, Gram-positive (G+) bacteria and Gram-negative (G−) bacteria between tree species and along the soil depth gradients. The fourth study investigated the molecular composition of extractable soil microbial biomass-derived (SMB) and SOM-derived compounds by electrospray ionization Fourier transformation ion cyclotron resonance mass spectrometry (ESI-FT-ICR-MS). This was complemented by the analysis of nine monosaccharides representing microbial or plant origin. Microbially derived compounds substantially contributed to SOM and the contribution increased with soil depth. The supply of tree species-specific substrates resulted in different chemical composition of SMB with largest differences between deciduous and coniferous stands. At the same time, microorganisms contributed to SOM resulting in a strong similarity in the composition of SOM and SMB.
Overall, the complex interplay of tree species-specific litter inputs and the ability, activity and efficiency of the associated soil fauna and microbial community in metabolizing the organic substrates leads to significant differences in the amount, distribution, quality and consequently, the stability of SOM. These findings are useful for a targeted cultivation of tree species to optimize soil C sequestration and other forest ecosystems services.
In order to classify smooth foliated manifolds, which are smooth maifolds equipped with a smooth foliation, we introduce the de Rham cohomologies of smooth foliated manifolds. These cohomologies are build in a similar way as the de Rham cohomologies of smooth manifolds. We develop some tools to compute these cohomologies. For example we proof a Mayer Vietoris theorem for foliated de Rham cohomology and show that these cohomologys are invariant under integrable homotopy. A generalization of a known Künneth formula, which relates the cohomologies of a product foliation with its factors, is discussed. In particular, this envolves a splitting theory of sequences between Frechet spaces and a theory of projective spectrums. We also prove, that the foliated de Rham cohomology is isomorphic to the Cech-de Rham cohomology and the Cech cohomology of leafwise constant functions of an underlying so called good cover.
The present work explores how theories of motivation can be used to enhance video game research. Currently, Flow-Theory and Self-Determination Theory are the most common approaches in the field of Human-Computer Interaction. The dissertation provides an in-depth look into Motive Disposition Theory and how to utilize it to explain interindividual differences in motivation. Different players have different preferences and make different choices when playing games, and not every player experiences the same outcomes when playing the same game. I provide a short overview of the current state of the research on motivation to play video games. Next, Motive Disposition Theory is applied in the context of digital games in four different research papers, featuring seven studies, totaling 1197 participants. The constructs of explicit and implicit motives are explained in detail while focusing on the two social motives (i.e., affiliation and power). As dependent variables, behaviour, preferences, choices, and experiences are used in different game environments (i.e., Minecraft, League of Legends, and Pokémon). The four papers are followed by a general discussion about the seven studies and Motive Disposition Theory in general. Finally, a short overview is provided about other theories of motivation and how they could be used to further our understanding of the motivation to play digital games in the future. This thesis proposes that 1) Motive Disposition Theory represents a valuable approach to understand individual motivations within the context of digital games; 2) there is a variety of motivational theories that can and should be utilized by researchers in the field of Human-Computer Interaction to broaden the currently one-sided perspective on human motivation; 3) researchers should aim to align their choice of motivational theory with their research goals by choosing the theory that best describes the phenomenon in question and by carefully adjusting each study design to the theoretical assumptions of that theory.
In her poems, Tawada constructs liminal speaking subjects – voices from the in-between – which disrupt entrenched binary thought processes. Synthesising relevant concepts from theories of such diverse fields as lyricology, performance studies, border studies, cultural and postcolonial studies, I develop ‘voice’ and ‘in-between space’ as the frameworks to approach Tawada’s multifaceted poetic output, from which I have chosen 29 poems and two verse novels for analysis. Based on the body speaking/writing, sensuality is central to Tawada’s use of voice, whereas the in-between space of cultures and languages serves as the basis for the liminal ‘exophonic’ voices in her work. In the context of cultural alterity, Tawada focuses on the function of language, both its effect on the body and its role in subject construction, while her feminist poetry follows the general development of feminist academia from emancipation to embodiment to queer representation. Her response to and transformation of écriture féminine in her verse novels transcends the concept of the body as the basis of identity, moving to literary and linguistic, plural self-construction instead. While few poems are overtly political, the speaker’s personal and contextual involvement in issues of social conflict reveal the poems’ potential to speak of, and to, the multiply identified citizens of a globalised world, who constantly negotiate physical as well as psychological borders.
Teamwork is ubiquitous in the modern workplace. However, it is still unclear whether various behavioral economic factors de- or increase team performance. Therefore, Chapters 2 to 4 of this thesis aim to shed light on three research questions that address different determinants of team performance.
Chapter 2 investigates the idea of an honest workplace environment as a positive determinant of performance. In a work group, two out of three co-workers can obtain a bonus in a dice game. By misreporting a secret die roll, cheating without exposure is an option in the game. Contrary to claims on the importance of honesty at work, we do not observe a reduction in the third co-worker's performance, who is an uninvolved bystander when cheating takes place.
Chapter 3 analyzes the effect of team size on performance in a workplace environment in which either two or three individuals perform a real-effort task. Our main result shows that the difference in team size is not harmful to task performance on average. In our discussion of potential mechanisms, we provide evidence on ongoing peer effects. It appears that peers are able to alleviate the potential free-rider problem emerging out of working in a larger team.
In Chapter 4, the role of perceived co-worker attractiveness for performance is analyzed. The results show that task performance is lower, the higher the perceived attractiveness of co-workers, but only in opposite-sex constellations.
The following Chapter 5 analyzes the effect of offering an additional payment option in a fundraising context. Chapter 6 focuses on privacy concerns of research participants.
In Chapter 5, we conduct a field experiment in which, participants have the opportunity to donate for the continuation of an art exhibition by either cash or cash and an additional cashless payment option (CPO). The treatment manipulation is completed by framing the act of giving either as a donation or pay-what-you-want contribution. Our results show that donors shy away from using the CPO in all treatment conditions. Despite that, there is no negative effect of the CPO on the frequency of financial support and its magnitude.
In Chapter 6, I conduct an experiment to test whether increased transparency of data processing affects data disclosure and whether the results change if it is indicated that the implementation of the GDPR happened involuntarily. I find that increased transparency raises the number of participants who do not disclose personal data by 21 percent. However, this is not the case in the involuntary-signal treatment, where the share of non-disclosures is relatively high in both conditions.
The daily dose of health information: A psychological view on the health information seeking process
(2021)
The search for health information is becoming increasingly important in everyday life, as well as socially and scientifically relevant Previous studies have mainly focused on the design and communication of information. However, the view of the seeker as well as individual
differences in skills and abilities has been a neglected topic so far. A psychological perspective on the process of searching for health information would provide important starting points for promoting the general dissemination of relevant information and thus improving health behaviour and health status. Within the present dissertation, the process of seeking health information was thus divided into sequential stages to identify relevant personality traits and skills. Accordignly, three studies are presented that focus on one stage
of the process respectively and empirically test potential crucial traits and skills: Study I investigates possible determinants of an intention for a comprehensive search for health information. Building an intention is considered as the basic step of the search process.
Motivational dispositions and self-regulatory skills were related to each other in a structural equation model and empirically tested based on theoretical investigations. Model fit showed an overall good fit and specific direct and indirect effects from approach and avoidance
motivation on the intention to seek comprehensively could be found, which supports the theoretical assumptions. The results show that as early as the formation of intention, the psychological perspective reveals influential personality traits and skills. Study II deals with the subsequent step, the selection of information sources. The preference for basic characteristics of information sources (i.e., accessibility, expertise, and interaction) is related to health information literacy as a collective term for relevant skills and intelligence as a personality trait. Furthermore, the study considers the influence of possible over- or underestimation of these characteristics. The results show not only a different predictive
contribution of health literacy and intelligence, but also the relevance of subjective and objective measurement.
Finally, Study III deals with the selection and evaluation of the health information previously found. The phenomenon of selective exposure is analysed, as this can be considered problematic in the health context. For this purpose, an experimental design was implemented in which a varying health threat was suggested to the participants. Relevant information was presented and the selective choice of this information was assessed. Health literacy was tested
as a moderator in a function of the induced threat and perceived vulnerability, triggering defence motives on the degree of bias. Findings show the importance of the consideration of the defence motives, which could cause a bias in the form of selective exposure. Furthermore, health literacy even seems to amplify this effect.
Results of the three studies are synthesized, discussed and general conclusions are drawn and implications for further research are determined.
The Eurosystem's Household Finance and Consumption Survey (HFCS) collects micro data on private households' balance sheets, income and consumption. It is a stylised fact that wealth is unequally distributed and that the wealthiest own a large share of total wealth. For sample surveys which aim at measuring wealth and its distribution, this is a considerable problem. To overcome it, some of the country surveys under the HFCS umbrella try to sample a disproportionately large share of households that are likely to be wealthy, a technique referred to as oversampling. Ignoring such types of complex survey designs in the estimation of regression models can lead to severe problems. This thesis first illustrates such problems using data from the first wave of the HFCS and canonical regression models from the field of household finance and gives a first guideline for HFCS data users regarding the use of replicate weight sets for variance estimation using a variant of the bootstrap. A further investigation of the issue necessitates a design-based Monte Carlo simulation study. To this end, the already existing large close-to-reality synthetic simulation population AMELIA is extended with synthetic wealth data. We discuss different approaches to the generation of synthetic micro data in the context of the extension of a synthetic simulation population that was originally based on a different data source. We propose an additional approach that is suitable for the generation of highly skewed synthetic micro data in such a setting using a multiply-imputed survey data set. After a description of the survey designs employed in the first wave of the HFCS, we then construct new survey designs for AMELIA that share core features of the HFCS survey designs. A design-based Monte Carlo simulation study shows that while more conservative approaches to oversampling do not pose problems for the estimation of regression models if sampling weights are properly accounted for, the same does not necessarily hold for more extreme oversampling approaches. This issue should be further analysed in future research.
This thesis contributes to the economic literature on India and specifically focuses on investment project (IP) location choice. I study three topics that naturally arise in sequence: geographic concentration of investment projects, the determinants of the location choices, and the impact these choices have on project success.
In Chapter 2, I provide the analysis of geographic concentration of IPs. I find that investments were concentrated over the period of observation (1996–2015), although the degree of concentration was decreasing. Additionally, I analyze different subsamples of the data set by ownership (Indian private, Indian public and foreign) and project status (completed or dropped). Foreign projects in all industries are more concentrated than private and public, while for the latter categories I identify only minor differences in concentration levels. Additionally, I find that the location patterns of completed and dropped investments are similar to that of the overall distribution and the distributions of their respective industries with completed IPs being somewhat more concentrated.
In Chapter 3, I study the determinants of project location choices with the focus on an important highway upgrade, the Golden Quadrilateral (GQ). In line with the existing literature, the GQ construction is connected to higher levels of investment in the affected non-nodal GQ districts in 2002–2016. I also provide suggestive evidence on changes in firm behavior after the GQ construction: Firms located in the non-nodal GQ districts became less likely to invest in their neighbor districts after the GQ completion compared to firms located in districts unaffected by the GQ construction.
Finally, in Chapter 4, I investigate the characteristics of IPs that may contribute to discontinuation of their implementation by comparing completed investments to dropped ones, defined as abandoned, shelved, and stalled investments as identified on the date of the data download. Controlling for local and business cycle conditions, as well as various investor and project characteristics, I show that projects located in close proximity to the investor offices (i.e., in the same district) are more likely to achieve the completion stage than more remote projects.
This thesis focus on threats as an experience of stress. Threats are distinguished from challenges and hindrances as another dimension of stress in challenge-hindrance models (CHM) of work stress (Tuckey et al., 2015). Multiple disciplines of psychology (e.g. stereotype, Fingerhut & Abdou, 2017; identity, Petriglieri, 2011) provide a variety of possible events that can trigger threats (e.g., failure expe-riences, social devaluation; Leary et al., 2009). However, systematic consideration of triggers and thus, an overview of when does the danger of threats arises, has been lacking to date. The explanation why events are appraised as threats is related to frustrated needs (e.g., Quested et al., 2011; Semmer et al., 2007), but empirical evidence is rare and needs can cover a wide range of content (e.g., relatedness, competence, power), depending on need approaches (e.g., Deci & Ryan, 2000; McClelland, 1961). This thesis aims to shed light on triggers (when) and the need-based mechanism (why) of threats.
In the introduction, I introduce threats as a dimension of stress experience (cf. Tuckey et al., 2015) and give insights into the diverse field of threat triggers (the when of threats). Further, I explain threats in terms of a frustrated need for positive self-view, before presenting specific needs as possible deter-minants in the threat mechanism (the why of threats). Study 1 represents a literature review based on 122 papers from interdisciplinary threat research and provides a classification of five triggers and five needs identified in explanations and operationalizations of threats. In Study 2, the five triggers and needs are ecologically validated in interviews with police officers (n = 20), paramedics (n = 10), teach-ers (n = 10), and employees of the German federal employment agency (n = 8). The mediating role of needs in the relationship between triggers and threats is confirmed in a correlative survey design (N = 101 Leaders working part-time, Study 3) and in a controlled laboratory experiment (N = 60 two-person student teams, Study 4). The thesis ends with a general discussion of the results of the four studies, providing theoretical and practical implications.
Internet interventions have gained popularity and the idea is to use them to increase the availability of psychological treatment. Research suggests that internet interventions are effective for a number of psychological disorders with effect sizes comparable to those found in face-to-face treatment. However, when provided as an add-on to treatment as usual, internet interventions do not seem to provide additional benefit. Furthermore, adherence and dropout rates vary greatly between studies, limiting the generalizability of the findings. This underlines the need to further investigate differences between internet interventions, participating patients, and their usage of interventions. A stronger focus on the processes of change seems necessary to better understand the varying findings regarding outcome, adherence and dropout in internet interventions. Thus, the aim of this dissertation was to investigate change processes in internet interventions and the factors that impact treatment response. This could help to identify important variables that should be considered in research on internet interventions as well as in clinical settings that make use of internet interventions.
Study I (Chapter 5) investigated early change patterns in participants of an internet intervention targeting depression. Data from 409 participants were analyzed using Growth Mixture Modeling. Specifically a piecewise model was applied to model change from screening to registration (pretreatment) and early change (registration to week four of treatment). Three early change patterns were identified; two were characterized by improvement and one by deterioration. The patterns were predictive of treatment outcome. The results therefore indicated that early change should be closely monitored in internet interventions, as early change may be an important indicator of treatment outcome.
Study II (Chapter 6) picked up on the idea of analyzing change patterns in internet interventions and extended it by using the Muthen-Roy model to identify change-dropout patterns. A sligthly bigger sample of the dataset from Study I was analyzed (N = 483). Four change-dropout patterns emerged; high risk of dropout was associated with rapid improvement and deterioration. These findings indicate that clinicians should consider how dropout may depend on patient characteristics as well as symptom change, as dropout is associated with both deterioration and a good enough dosage of treatment.
Study III (Chapter 7) compared adherence and outcome in different participant groups and investigated the impact of adherence to treatment components on treatment outcome in an internet intervention targeting anxiety symptoms. 50 outpatient participants waiting for face- to-face treatment and 37 self-referred participants were compared regarding adherence to treatment components and outcome. In addition, outpatient participants were compared to a matched sample of outpatients, who had no access to the internet intervention during the waiting period. Adherence to treatment components was investigated as a predictor of treatment outcome. Results suggested that especially adherence may vary depending on participant group. Also using specific measures of adherence such as adherence to treatment components may be crucial to detect change mechanisms in internet interventions. Fostering adherence to treatment components in participants may increase the effectiveness of internet interventions.
Results of the three studies are discussed and general conclusions are drawn.
Implications for future research as well as their utility for clinical practice and decision- making are presented.
Estimation and therefore prediction -- both in traditional statistics and machine learning -- encounters often problems when done on survey data, i.e. on data gathered from a random subset of a finite population. Additional to the stochastic generation of the data in the finite population (based on a superpopulation model), the subsetting represents a second randomization process, and adds further noise to the estimation. The character and impact of the additional noise on the estimation procedure depends on the specific probability law for subsetting, i.e. the survey design. Especially when the design is complex or the population data is not generated by a Gaussian distribution, established methods must be re-thought. Both phenomena can be found in business surveys, and their combined occurrence poses challenges to the estimation.
This work introduces selected topics linked to relevant use cases of business surveys and discusses the role of survey design therein: First, consider micro-econometrics using business surveys. Regression analysis under the peculiarities of non-normal data and complex survey design is discussed. The focus lies on mixed models, which are able to capture unobserved heterogeneity e.g. between economic sectors, when the dependent variable is not conditionally normally distributed. An algorithm for survey-weighted model estimation in this setting is provided and applied to business data.
Second, in official statistics, the classical sampling randomization and estimators for finite population totals are relevant. The variance estimation of estimators for (finite) population totals plays a major role in this framework in order to decide on the reliability of survey data. When the survey design is complex, and the number of variables is large for which an estimated total is required, generalized variance functions are popular for variance estimation. They allow to circumvent cumbersome theoretical design-based variance formulae or computer-intensive resampling. A synthesis of the superpopulation-based motivation and the survey framework is elaborated. To the author's knowledge, such a synthesis is studied for the first time both theoretically and empirically.
Third, the self-organizing map -- an unsupervised machine learning algorithm for data visualization, clustering and even probability estimation -- is introduced. A link to Markov random fields is outlined, which to the author's knowledge has not yet been established, and a density estimator is derived. The latter is evaluated in terms of a Monte-Carlo simulation and then applied to real world business data.
Die Polargebiete sind geprägt von harschen Umweltbedingungen mit extrem kalten Temperaturen und Winden. Besonders während der polaren Nacht werden Temperaturen von bis zu -89.2°C}$ auf dem Antarktischen Plateau beobachtet. Infolge der starken Abkühlung beginnt das Ozeanwasser zu gefrieren und die Eisproduktion beginnt. Der Antarktische Ozean ist dabei von einer ausgeprägten zwischen- und innerjährlichen Variabilität geprägt und die Eisbedeckung variiert zwischen 2.07 * 10^6 km^2 im Sommer und 20.14 * 10^6 km^2 im Winter. Die Eisproduktion und Eisschmelze beeinflussen die atmosphärische und ozeanische Zirkulation. Dynamische Prozesse führen zur Bildung von Rissen im Eis und letztlich zum Entstehen von Eisrinnen (leads). Leads sind langgestreckte Risse die mindestens einige Meter breit und hunderte Meter bis hunderte Kilometer lang sein können. In diesen Eisrinnen ist das warme Ozeanwasser in Kontakt mit der kalten Atmosphäre, wodurch die Austauschraten fühlbarer und latenter Wärme, Feuchtigkeit und von Gasen stark erhöht sind. Eisrinnen tragen zur Eisproduktion in den Polargebieten bei und sind Habitat für zahlreiche Tiere. Eisrinnen, zentraler Bestandteil der präsentierten Studie, sind bis heute nur unzureichend im Südpolarmeer erforscht und beobachtet. Daher ist es Ziel einen Algorithmus zu entwickeln, um Eisrinnen in Fernerkundungsdaten automatisiert zu identifizieren. Dabei kommen thermal-Infrarot Satellitendaten des Moderate-Resolution Imaging Spectroradiometer (MODIS) zum Einsatz, welches auf den beiden Satelliten Aqua und Terra montiert ist und seit 2000 (Terra) bzw. 2002 (Aqua) Satellitenbilder bereitstellt. Die einzelnen Satellitenbilder beinhalten die Eisoberflächentemperatur des MOD/MYD 29 Produktes, welche in einem zweistufigen Algorithmus für den Zeitraum April bis September 2003 bis 2019 prozessiert werden.
Im ersten Schritt werden potentielle Eisrinnen anhand der lokalen positiven Temperaturanomalie identifiziert. Aufgrund von Artefakten werden weitere temperatur- und texturbasierte Parameter abgeleitet und zu täglichen Kompositen zusammengefügt. Diese werden in der zweiten Prozessierungsstufe verwendet, um Wolkenartefakte von echten Eisrinnen-Observationen zu trennen. Hier wird Fuzzy Logic genutzt und eine Antarktis-spezifische Konfiguration wird definiert. In diesem werden ausgewählte Eingabedaten aus dem ersten Prozessierungslevel genutzt, um einen finalen Proxy, den Lead Score (LS), zu berechnen. Der LS wird abschließend mittels manueller Qualitätskontrolle in eine Unsicherheit überführt. Die darüber identifizierten Artefakte können so zusätzlich zur MODIS-Wolkenmaske genutzt werden.
Auf Basis der Eisrinnenbeobachtungen wird ein klimatologischer Referenzdatensatz erstellt, der die repräsentative Eisrinnenverteilung im Antarktischen Ozean für die Wintermonate April bis September, 2003 bis 2019 zeigt. In diesem ist sichtbar, dass Eisrinnen in manchen Gegenden systematischer auftreten als in anderen. Das sind vor allem die Regionen entlang der Küstenregion, des kontinentalen Schelfabhangs und einigen Erhebungen und Kanälen in der Tiefsee. Dabei sind die erhöhten Frequenzen entlang des Schelfabhangs besonders interessant und der Einfluss von atmosphärischen und ozeanischen Einflüssen wird untersucht. Ein regionales Eis-Ozeanmodell wird genutzt, um ozeanische Einflüsse in Zusammenhang mit erhöhten Eisrinnenfrequenzen zu setzen.
In der vorliegenden Studie wird außerdem ein umfangreicher Überblick über die großskalige Variabilität von Antarktischem Meereis gegeben. Tägliche Eiskonzentrationsdaten, abgeleitet aus passiven Mikrowellendaten, werden aus dem Zeitraum 1979 bis 2018 für die Klassifikation genutzt. Der dk-means Algorithmus wird verwendet, um zehn repräsentative Eisklassen zu identifizieren. Die geographische Verteilung dieser Klassen wird als Karte dargestellt, in der der typische jährliche Eiszyklus je Klasse sichtbar ist.
Veränderungen in dem räumlichen Auftreten von Eisklassen werden identifiziert und qualitativ interpretiert. Positive Abweichungen hin zu höheren Eisklassen werden im Weddell- und dem Ross-Meer und einigen Regionen in der Ostantarktis identifiziert. Negative Abweichungen sind im Amundsen-Bellingshausen-Meer vorhanden. Der neu entwickelte (Climatological Sea Ice Anomaly Index) wird genutzt, um Klassenabweichungen in der Zeitreihe zu identifizieren. Damit werden drei Jahre (1986, 2007, 2014) für eine Fallstudie ausgewählt und in Relation zu atmosphärischen Daten aus ERA-Interim und Eisdrift-Daten untersucht. Für die beiden Jahre 1986 und 2007 können bestimmte atmosphärische Zirkulationsmuster identifiziert werden, die die entsprechende Eisklassifikation beeinflusst haben. Für das Jahr 2014 können keine besonders ausgeprägten atmosphärischen Anomalien ausgemacht werden.
Der Eisklassen-Datensatz kann in Zukunft als Ergänzung zu vorhandenen Studien und für die Validierung von Meereismodellen genutzt werden. Dabei sind vor allem Anwendungen in Bezug auf den Eisrinnen-Datensatz möglich.
Evidence points to autonomy as having a place next to affiliation, achievement, and power as one of the basic implicit motives; however, there is still some research that needs to be conducted to support this notion.
The research in this dissertation aimed to address this issue. I have specifically focused on two issues that help solidify the foundation of work that has already been conducted on the implicit autonomy motive, and will also be a foundation for future studies. The first issue is measurement. Implicit motives should be measured using causally valid instruments (McClelland, 1980). The second issue addresses the function of motives. Implicit motives orient, select, and energize behavior (McClelland, 1980). If autonomy is an implicit motive, then we need a valid instrument to measure it and we also need to show that it orients, selects, and energizes behavior.
In the following dissertation, I address these two issues in a series of ten studies. Firstly, I present studies that examine the causal validity of the Operant Motive Test (OMT; Kuhl, 2013) for the implicit affiliation and power motives using established methods. Secondly, I developed and empirically tested pictures to specifically assess the implicit autonomy motive and examined their causal validity. Thereafter, I present two studies that investigated the orienting and energizing effects of the implicit autonomy motive. The results of the studies solidified the foundation of the OMT and how it measures nAutonomy. Furthermore, this dissertation demonstrates that nAutonomy fulfills the criteria for two of the main functions of implicit motives. Taken together, the findings of this dissertation provide further support for autonomy as an implicit motive and a foundation for intriguing future studies.
Imagery-based techniques have received increasing interest in psychotherapy research. Whereas their effectiveness has been shown for various psychological disorders, their underlying mechanisms remain unclear. Current research predominantly investigates intrapersonal processes, while interpersonal processes have received no attention to date. The aim of the current dissertation was to fill this lacuna. The three interrelated studies comprising this dissertation were the first to examine the effectiveness of imagery-based techniques in the treatment of test anxiety, relate physiological arousal to emotional processing, and investigate the association between physiological synchrony and multiple process measures.
Study I investigated the feasibility of a newly developed protocol, which integrates imagery-based and cognitive-behavioral components, to treat test anxiety in a sample of 31 students. The results indicated the protocol as acceptable, feasible, and effective in the treatment of test anxiety. Additionally, the imagery-based component was positively associated with therapeutic bond, session evaluation, and emotional experience.
Study II shifted the focus from the effectiveness of imagery-based techniques to client-therapist physiological synchrony as a putative mechanism of change in the same sample. The results suggested that physiological synchrony was greater than chance during both imagery-based and cognitive-behavioral components. Variability of physiological synchrony on the session-level during the imagery-based components and variability on both levels (session and dyad) during the cognitive-behavioral components were demonstrated. Furthermore, physiological synchrony of the imagery-based segments was positively assocatied with therapeutic bond. No association was found for the cognitive-behavioral components.
Study III examined both intrapersonal (i.e., clients’ electrodermal activity) and interpersonal (i.e., client-therapist electrodermal activity synchrony) processes and their associations with emotional processing in a sample of 49 client-therapist-dyads. The results suggested that higher client physiological arousal and a moderate level of physiological synchrony were associated with deeper emotional processing.
Taken together, the results highlight the effectiveness of imagery-based techniques in the treatment of test anxiety. Furthermore, the results of Studies II and III support the idea of physiological synchrony as a mechanism of change in imagery with and without rescripting. The current dissertation takes an important step towards optimizing process research within psychotherapy and contributes to a better understanding of the potency and mechanisms of change of imagery-based techniques. We hope that these studies’ implications will support everyday clinical practice.
The dissertation includes three published articles on which the development of a theoretical model of motivational and self-regulatory determinants of the intention to comprehensively search for health information is based. The first article focuses on building a solid theoretical foundation as to the nature of a comprehensive search for health information and enabling its integration into a broader conceptual framework. Based on subjective source perceptions, a taxonomy of health information sources was developed. The aim of this taxonomy was to identify most fundamental source characteristics to provide a point of reference when it comes to relating to the target objects of a comprehensive search. Three basic source characteristics were identified: expertise, interaction and accessibility. The second article reports on the development and evaluation of an instrument measuring the goals individuals have when seeking health information: the ‘Goals Associated with Health Information Seeking’ (GAINS) questionnaire. Two goal categories (coping focus and regulatory focus) were theoretically derived, based on which four goals (understanding, action planning, hope and reassurance) were classified. The final version of the questionnaire comprised four scales representing the goals, with four items per scale (sixteen items in total). The psychometric properties of the GAINS were analyzed in three independent samples, and the questionnaire was found to be reliable and sufficiently valid as well as suitable for a patient sample. It was concluded that the GAINS makes it possible to evaluate goals of health information seeking (HIS) which are likely to inform the intention building on how to organize the search for health information. The third article describes the final development and a first empirical evaluation of a model of motivational and self-regulatory determinants of an intentionally comprehensive search for health information. Based on the insights and implications of the previous two articles and an additional rigorous theoretical investigation, the model included approach and avoidance motivation, emotion regulation, HIS self-efficacy, problem and emotion focused coping goals and the intention to seek comprehensively (as outcome variable). The model was analyzed via structural equation modeling in a sample of university students. Model fit was good and hypotheses with regard to specific direct and indirect effects were confirmed. Last, the findings of all three articles are synthesized, the final model is presented and discussed with regard to its strengths and weaknesses, and implications for further research are determined.
Structured Eurobonds - Optimal Construction, Impact on the Euro and the Influence of Interest Rates
(2020)
Structured Eurobonds are a prominent topic in the discussions how to complete the monetary and fiscal union. This work sheds light on several issues going hand in hand with the introduction of common bonds. At first a crucial question is on the optimal construction, e.g. what is the optimal common liability. Other questions that arise belong to the time after the introduction. The impact on several exchnage rates is examined in this work. Finally an approximation bias in forward-looking DSGE models is quantified which would lead to an adjustment of central bank interest rates and therefore has an impact on the other two topics.
The formerly communist countries in Central and Eastern Europe (transitional economies in Europe and the Soviet Union – for example, East Germany, Czech Republic, Hungary, Lithuania, Poland, Russia) and transitional economies in Asia – for example, China, Vietnam had centrally planned economies, which did not allow entrepreneurship activities. Despite the political-socioeconomic transformations in transitional economies around 1989, they still had an institutional heritage that affects individuals’ values and attitudes, which, in turn, influence intentions, behaviors, and actions, including entrepreneurship.
While prior studies on the long-lasting effects of socialist legacy on entrepreneurship have focused on limited geographical regions (e.g., East-West Germany, and East-West Europe), this dissertation focuses on the Vietnamese context, which offers a unique quasi-experimental setting. In 1954, Vietnam was divided into the socialist North and the non-socialist South, and it was then reunified under socialist rule in 1975. Thus, the intensity of differences in socialist treatment in North-South Vietnam (about 21 years) is much shorter than that in East-West Germany (about 40 years) and East-West Europe (about 70 years when considering former Soviet Union countries).
To assess the relationship between socialist history and entrepreneurship in this unique setting, we survey more than 3,000 Vietnamese individuals. This thesis finds that individuals from North Vietnam have lower entrepreneurship intentions, are less likely to enroll in entrepreneurship education programs, and display lower likelihood to take over an existing business, compared to those from the South of Vietnam. The long-lasting effect of formerly socialist institutions on entrepreneurship is apparently deeper than previously discovered in the prominent case of East-West Germany and East-West Europe as well.
In the second empirical investigation, this dissertation focuses on how succession intentions differ from others (e.g., founding, and employee intentions) regarding career choice motivation, and the effect of three main elements of the theory of planned behavior (e.g., entrepreneurial attitude, subjective norms, and perceived behavioral control) in transition economy – Vietnam context. The findings of this thesis suggest that an intentional founder is labeled with innovation, an intentional successor is labeled with roles motivation, and an intentional employee is labeled with social mission. Additionally, this thesis reveals that entrepreneurial attitude and perceived behavioral control are positively associated with the founding intention, whereas there is no difference in this effect between succession and employee intentions.
Traditionell werden Zufallsstichprobenerhebungen so geplant, dass nationale Statistiken zuverlässig mit einer adäquaten Präzision geschätzt werden können. Hierbei kommen vorrangig designbasierte, Modell-unterstützte (engl. model assisted) Schätzmethoden zur Anwendung, die überwiegend auf asymptotischen Eigenschaften beruhen. Für kleinere Stichprobenumfänge, wie man sie für Small Areas (Domains bzw. Subpopulationen) antrifft, eignen sich diese Schätzmethoden eher nicht, weswegen für diese Anwendung spezielle modellbasierte Small Area-Schätzverfahren entwickelt wurden. Letztere können zwar Verzerrungen aufweisen, besitzen jedoch häufig einen kleineren mittleren quadratischen Fehler der Schätzung als dies für designbasierte Schätzer der Fall ist. Den Modell-unterstützten und modellbasierten Methoden ist gemeinsam, dass sie auf statistischen Modellen beruhen; allerdings in unterschiedlichem Ausmass. Modell-unterstützte Verfahren sind in der Regel so konstruiert, dass der Beitrag des Modells bei sehr grossen Stichprobenumfängen gering ist (bei einer Grenzwertbetrachtung sogar wegfällt). Bei modellbasierten Methoden nimmt das Modell immer eine tragende Rolle ein, unabhängig vom Stichprobenumfang. Diese Überlegungen veranschaulichen, dass das unterstellte Modell, präziser formuliert, die Güte der Modellierung für die Qualität der Small Area-Statistik von massgeblicher Bedeutung ist. Wenn es nicht gelingt, die empirischen Daten durch ein passendes Modell zu beschreiben und mit den entsprechenden Methoden zu schätzen, dann können massive Verzerrungen und / oder ineffiziente Schätzungen resultieren.
Die vorliegende Arbeit beschäftigt sich mit der zentralen Frage der Robustheit von Small Area-Schätzverfahren. Als robust werden statistische Methoden dann bezeichnet, wenn sie eine beschränkte Einflussfunktion und einen möglichst hohen Bruchpunkt haben. Vereinfacht gesprochen zeichnen sich robuste Verfahren dadurch aus, dass sie nur unwesentlich durch Ausreisser und andere Anomalien in den Daten beeinflusst werden. Die Untersuchung zur Robustheit konzentriert sich auf die folgenden Modelle bzw. Schätzmethoden:
i) modellbasierte Schätzer für das Fay-Herriot-Modell (Fay und Herrot, 1979, J. Amer. Statist. Assoc.) und das elementare Unit-Level-Modell (vgl. Battese et al., 1988, J. Amer. Statist. Assoc.).
ii) direkte, Modell-unterstützte Schätzer unter der Annahme eines linearen Regressionsmodells.
Das Unit-Level-Modell zur Mittelwertschätzung beruht auf einem linearen gemischten Gauss'schen Modell (engl. mixed linear model, MLM) mit blockdiagonaler Kovarianzmatrix. Im Gegensatz zu bspw. einem multiplen linearen Regressionsmodell, besitzen MLM-Modelle keine nennenswerten Invarianzeigenschaften, so dass eine Kontamination der abhängigen Variablen unvermeidbar zu verzerrten Parameterschätzungen führt. Für die Maximum-Likelihood-Methode kann die resultierende Verzerrung nahezu beliebig groß werden. Aus diesem Grund haben Richardson und Welsh (1995, Biometrics) die robusten Schätzmethoden RML 1 und RML 2 entwickelt, die bei kontaminierten Daten nur eine geringe Verzerrung aufweisen und wesentlich effizienter sind als die Maximum-Likelihood-Methode. Eine Abwandlung von Methode RML 2 wurde Sinha und Rao (2009, Canad. J. Statist.) für die robuste Schätzung von Unit-Level-Modellen vorgeschlagen. Allerdings erweisen sich die gebräuchlichen numerischen Verfahren zur Berechnung der RML-2-Methode (dies gilt auch für den Vorschlag von Sinha und Rao) als notorisch unzuverlässig. In dieser Arbeit werden zuerst die Konvergenzprobleme der bestehenden Verfahren erörtert und anschließend ein numerisches Verfahren vorgeschlagen, das sich durch wesentlich bessere numerische Eigenschaften auszeichnet. Schließlich wird das vorgeschlagene Schätzverfahren im Rahmen einer Simulationsstudie untersucht und anhand eines empirischen Beispiels zur Schätzung von oberirdischer Biomasse in norwegischen Kommunen illustriert.
Das Modell von Fay-Herriot kann als Spezialfall eines MLM mit blockdiagonaler Kovarianzmatrix aufgefasst werden, obwohl die Varianzen des Zufallseffekts für die Small Areas nicht geschätzt werden müssen, sondern als bereits bekannte Größen betrachtet werden. Diese Eigenschaft kann man sich nun zunutze machen, um die von Sinha und Rao (2009) vorgeschlagene Robustifizierung des Unit-Level-Modells direkt auf das Fay-Herriot Model zu übertragen. In der vorliegenden Arbeit wird jedoch ein alternativer Vorschlag erarbeitet, der von der folgenden Beobachtung ausgeht: Fay und Herriot (1979) haben ihr Modell als Verallgemeinerung des James-Stein-Schätzers motiviert, wobei sie sich einen empirischen Bayes-Ansatz zunutze machen. Wir greifen diese Motivation des Problems auf und formulieren ein analoges robustes Bayes'sches Verfahren. Wählt man nun in der robusten Bayes'schen Problemformulierung die ungünstigste Verteilung (engl. least favorable distribution) von Huber (1964, Ann. Math. Statist.) als A-priori-Verteilung für die Lokationswerte der Small Areas, dann resultiert als Bayes-Schätzer [=Schätzer mit dem kleinsten Bayes-Risk] die Limited-Translation-Rule (LTR) von Efron und Morris (1971, J. Amer. Statist. Assoc.). Im Kontext der frequentistischen Statistik kann die Limited-Translation-Rule nicht verwendet werden, weil sie (als Bayes-Schätzer) auf unbekannten Parametern beruht. Die unbekannten Parameter können jedoch nach dem empirischen Bayes-Ansatz an der Randverteilung der abhängigen Variablen geschätzt werden. Hierbei gilt es zu beachten (und dies wurde in der Literatur vernachlässigt), dass die Randverteilung unter der ungünstigsten A-priori-Verteilung nicht einer Normalverteilung entspricht, sondern durch die ungünstigste Verteilung nach Huber (1964) beschrieben wird. Es ist nun nicht weiter erstaunlich, dass es sich bei den Maximum-Likelihood-Schätzern von Regressionskoeffizienten und Modellvarianz unter der Randverteilung um M-Schätzer mit der Huber'schen psi-Funktion handelt.
Unsere theoriegeleitete Herleitung von robusten Schätzern zum Fay-Herriot-Modell zeigt auf, dass bei kontaminierten Daten die geschätzte LTR (mit Parameterschätzungen nach der M-Schätzmethodik) optimal ist und, dass die LTR ein integraler Bestandteil der Schätzmethodik ist (und nicht als ``Zusatz'' o.Ä. zu betrachten ist, wie dies andernorts getan wird). Die vorgeschlagenen M-Schätzer sind robust bei Vorliegen von atypischen Small Areas (Ausreissern), wie dies auch die Simulations- und Fallstudien zeigen. Um auch Robustheit bei Vorkommen von einflussreichen Beobachtungen in den unabhängigen Variablen zu erzielen, wurden verallgemeinerte M-Schätzer (engl. generalized M-estimator) für das Fay-Herriot-Modell entwickelt.
In this thesis we study structure-preserving model reduction methods for the efficient and reliable approximation of dynamical systems. A major focus is the approximation of a nonlinear flow problem on networks, which can, e.g., be used to describe gas network systems. Our proposed approximation framework guarantees so-called port-Hamiltonian structure and is general enough to be realizable by projection-based model order reduction combined with complexity reduction. We divide the discussion of the flow problem into two parts, one concerned with the linear damped wave equation and the other one with the general nonlinear flow problem on networks.
The study around the linear damped wave equation relies on a Galerkin framework, which allows for convenient network generalizations. Notable contributions of this part are the profound analysis of the algebraic setting after space-discretization in relation to the infinite dimensional setting and its implications for model reduction. In particular, this includes the discussion of differential-algebraic structures associated to the network-character of our problem and the derivation of compatibility conditions related to fundamental physical properties. Amongst the different model reduction techniques, we consider the moment matching method to be a particularly well-suited choice in our framework.
The Galerkin framework is then appropriately extended to our general nonlinear flow problem. Crucial supplementary concepts are required for the analysis, such as the partial Legendre transform and a more careful discussion of the underlying energy-based modeling. The preservation of the port-Hamiltonian structure after the model-order- and complexity-reduction-step represents a major focus of this work. Similar as in the analysis of the model order reduction, compatibility conditions play a crucial role in the analysis of our complexity reduction, which relies on a quadrature-type ansatz. Furthermore, energy-stable time-discretization schemes are derived for our port-Hamiltonian approximations, as structure-preserving methods from literature are not applicable due to our rather unconventional parametrization of the solution.
Apart from the port-Hamiltonian approximation of the flow problem, another topic of this thesis is the derivation of a new extension of moment matching methods from linear systems to quadratic-bilinear systems. Most system-theoretic reduction methods for nonlinear systems rely on multivariate frequency representations. Our approach instead uses univariate frequency representations tailored towards user-defined families of inputs. Then moment matching corresponds to a one-dimensional interpolation problem rather than to a multi-dimensional interpolation as for the multivariate approaches, i.e., it involves fewer interpolation frequencies to be chosen. The notion of signal-generator-driven systems, variational expansions of the resulting autonomous systems as well as the derivation of convenient tensor-structured approximation conditions are the main ingredients of this part. Notably, our approach allows for the incorporation of general input relations in the state equations, not only affine-linear ones as in existing system-theoretic methods.
Data used for the purpose of machine learning are often erroneous. In this thesis, p-quasinorms (p<1) are employed as loss functions in order to increase the robustness of training algorithms for artificial neural networks. Numerical issues arising from these loss functions are addressed via enhanced optimization algorithms (proximal point methods; Frank-Wolfe methods) based on the (non-monotonic) Armijo-rule. Numerical experiments comprising 1100 test problems confirm the effectiveness of the approach. Depending on the parametrization, an average reduction of the absolute residuals of up to 64.6% is achieved (aggregated over 100 test problems).
The present thesis is devoted to a construction which defies generalisations about the prototypical English noun phrase (NP) to such an extent that it has been termed the Big Mess Construction (Berman 1974). As illustrated by the examples in (1) and (2), the NPs under study involve premodifying adjective phrases (APs) which precede the determiner (always realised in the form of the indefinite article a(n)) rather than following it.
(1) NoS had not been hijacked – that was too strong a word. (BNC: CHU 1766)
(2) He was prepared for a battle if the porter turned out to be as difficult a customer as his wife. (BNC: CJX 1755)
Previous research on the construction is largely limited to contributions from the realms of theoretical syntax and a number of cursory accounts in reference grammars. No comprehensive investigation of its realisations and uses has as yet been conducted. My thesis fills this gap by means of an exhaustive analysis of the construction on the basis of authentic language data retrieved from the British National Corpus (BNC). The corpus-based approach allows me to examine not only the possible but also the most typical uses of the construction. Moreover, while previous work has almost exclusively focused on the formal realisations of the construction, I investigate both its forms and functions.
It is demonstrated that, while the construction is remarkably flexible as concerns its possible realisations, its use is governed by probabilistic constraints. For example, some items occur much more frequently inside the degree item slot than others (as, too and so stand out for their particularly high frequency). Contrary to what is assumed in most previous descriptions, the slot is not restricted in its realisation to a fixed number of items. Rather than representing a specialised structure, the construction is furthermore shown to be distributed over a wide range of possible text types and syntactic functions. On the other hand, it is found to be much less typical of spontaneous conversation than of written language; Big Mess NPs further display a strong preference for the function of subject complement. Investigations of the internal structural complexity of the construction indicate that its obligatory components can optionally be enriched by a remarkably wide range of optional (if infrequent) elements. In an additional analysis of the realisations of the obligatory but lexically variable slots (head noun and head of AP), the construction is highlighted to represent a productive pattern. With the help of the methods of Collexeme Analysis (Stefanowitsch and Gries 2003) and Co-varying Collexeme Analysis (Gries and Stefanowitsch 2004b, Stefanowitsch and Gries 2005), the two slots are, however, revealed to be strongly associated with general nouns and ‘evaluative’ and ‘dimension’ adjectives, respectively. On the basis of an inspection of the most typical adjective-noun combinations, I identify the prototypical semantics of the Big Mess Construction.
The analyses of the constructional functions centre on two distinct functional areas. First, I investigate Bolinger’s (1972) hypothesis that the construction fulfils functions in line with the Principle of Rhythmic Alternation (e.g. Selkirk 1984: 11, Schlüter 2005). It is established that rhythmic preferences co-determine the use of the construction to some extent, but that they clearly do not suffice to explain the phenomenon under study. In a next step, the discourse-pragmatic functions of the construction are scrutinised. Big Mess NPs are demonstrated to perform distinct information-structural functions in that the non-canonical position of the AP serves to highlight focal information (compare De Mönnink 2000: 134-35). Additionally, the construction is shown to place emphasis on acts of evaluation. I conclude the construction to represent a contrastive focus construction.
My investigations of the formal and functional characteristics of Big Mess NPs each include analyses which compare individual versions of the construction to one another (e.g. the As Big a Mess, Too Big a Mess and So Big a Mess Constructions). It is revealed that the versions are united by a shared core of properties while differing from one another at more abstract levels of description. The question of the status of the constructional versions as separate constructions further receives special emphasis as part of a discussion in which I integrate my results into the framework of usage-based Construction Grammar (e.g. Goldberg 1995, 2006).
This thesis addresses three different topics from the fields of mathematical finance, applied probability and stochastic optimal control. Correspondingly, it is subdivided into three independent main chapters each of which approaches a mathematical problem with a suitable notion of a stochastic particle system.
In Chapter 1, we extend the branching diffusion Monte Carlo method of Henry-Labordère et. al. (2019) to the case of parabolic PDEs with mixed local-nonlocal analytic nonlinearities. We investigate branching diffusion representations of classical solutions, and we provide sufficient conditions under which the branching diffusion representation solves the PDE in the viscosity sense. Our theoretical setup directly leads to a Monte Carlo algorithm, whose applicability is showcased in two stylized high-dimensional examples. As our main application, we demonstrate how our methodology can be used to value financial positions with defaultable, systemically important counterparties.
In Chapter 2, we formulate and analyze a mathematical framework for continuous-time mean field games with finitely many states and common noise, including a rigorous probabilistic construction of the state process. The key insight is that we can circumvent the master equation and reduce the mean field equilibrium to a system of forward-backward systems of (random) ordinary differential equations by conditioning on common noise events. We state and prove a corresponding existence theorem, and we illustrate our results in three stylized application examples. In the absence of common noise, our setup reduces to that of Gomes, Mohr and Souza (2013) and Cecchin and Fischer (2020).
In Chapter 3, we present a heuristic approach to tackle stochastic impulse control problems in discrete time. Based on the work of Bensoussan (2008) we reformulate the classical Bellman equation of stochastic optimal control in terms of a discrete-time QVI, and we prove a corresponding verification theorem. Taking the resulting optimal impulse control as a starting point, we devise a self-learning algorithm that estimates the continuation and intervention region of such a problem. Its key features are that it explores the state space of the underlying problem by itself and successively learns the behavior of the optimally controlled state process. For illustration, we apply our algorithm to a classical example problem, and we give an outlook on open questions to be addressed in future research.
Many NP-hard optimization problems that originate from classical graph theory, such as the maximum stable set problem and the maximum clique problem, have been extensively studied over the past decades and involve the choice of a subset of edges or vertices. There usually exist combinatorial methods that can be applied to solve them directly in the graph.
The most simple method is to enumerate feasible solutions and select the best. It is not surprising that this method is very slow oftentimes, so the task is to cleverly discard fruitless search space during the search. An alternative method to solve graph problems is to formulate integer linear programs, such that their solution yields an optimal solution to the original optimization problem in the graph. In order to solve integer linear programs, one can start with relaxing the integer constraints and then try to find inequalities for cutting off fractional extreme points. In the best case, it would be possible to describe the convex hull of the feasible region of the integer linear program with a set of inequalities. In general, giving a complete description of this convex hull is out of reach, even if it has a polynomial number of facets. Thus, one tries to strengthen the (weak) relaxation of the integer linear program best possible via strong inequalities that are valid for the convex hull of feasible integer points.
Many classes of valid inequalities are of exponential size. For instance, a graph can have exponentially many odd cycles in general and therefore the number of odd cycle inequalities for the maximum stable set problem is exponential. It is sometimes possible to check in polynomial time if some given point violates any of the exponentially many inequalities. This is indeed the case for the odd cycle inequalities for the maximum stable set problem. If a polynomial time separation algorithm is known, there exists a formulation of polynomial size that contains a given point if and only if it does not violate one of the (potentially exponentially many) inequalities. This thesis can be divided into two parts. The first part is the main part and it contains various new results. We present new extended formulations for several optimization problems, i.e. the maximum stable set problem, the nonconvex quadratic program with box
constraints and the p-median problem. In the second part we modify a very fast algorithm for finding a maximum clique in very large sparse graphs. We suggest and compare three alternative versions of this algorithm to the original version and compare their strengths and weaknesses.
Retirement, fertility and sexuality are three key life stage events that are embedded in the framework of population economics in this dissertation. Each topic implies economic relevance. As retirement entry shifts labour supply of experienced workers to zero, this issue is particularly relevant for employers, retirees themselves as well as policymakers who are in charge of the design of the pension system. Giving birth has comprehensive economic relevance for women. Parental leave and subsequent part-time work lead to a direct loss of income. Lower levels of employment, work experience, training and career opportunities result in indirect income losses. Sexuality has decisive influence on the quality of partnerships, subjective well-being and happiness. Well-being and happiness, in turn, are significant key determinants not only in private life but also in the work domain, for example in the area of job performance. Furthermore, partnership quality determines the duration of a partnership. And in general, partnerships enable the pooling of (financial) resources - compared to being single. The contribution of this dissertation emerges from the integration of social and psychological concepts into economic analysis as well as the application of economic theory in non-standard economic research topics. The results of the three chapters show that the multidisciplinary approach yields better prediction of human behaviour than the single disciplines on their own. The results in the first chapter show that both interpersonal conflict with superiors and the individual’s health status play a significant role in retirement decisions. The chapter further contributes to existing literature by showing the moderating role of health within the retirement decision-making: On the one hand, all employees are more likely to retire when they are having conflicts with their superior. On the other hand, among healthy employees, the same conflict raises retirement intentions even more. That means good health is a necessary, but not a sufficient condition for continued working. It may be that conflicts with superiors raise retirement intentions more if the worker is healthy. The key findings of the second chapter reveal significant influence of religion on contraceptive and fertility-related decisions. A large part of research on religion and fertility is originated in evidence from the US. This chapter contrasts evidence from Germany. Additionally, the chapter contributes by integrating miscarriages and abortions, rather than limiting the analysis to births and it gains from rich prospective data on fertility biography of women. The third chapter provides theoretical insights on how to incorporate psychological variables into an economic framework which aims to analyse sexual well-being. According to this theory, personality may play a dual role by shaping a person’s preferences for sex as well as the person’s behaviour in a sexual relationship. Results of econometric analysis reveal detrimental effects of neuroticism on sexual well-being while conscientiousness seems to create a win-win situation for a couple. Extraversions and Openness have ambiguous effects on romantic relationships by enhancing sexual well-being on the one hand but raising commitment problems on the other. Agreeable persons seem to gain sexual satisfaction even if they perform worse in sexual communication.
This work studies typical mathematical challenges occurring in the modeling and simulation of manufacturing processes of paper or industrial textiles. In particular, we consider three topics: approximate models for the motion of small inertial particles in an incompressible Newtonian fluid, effective macroscopic approximations for a dilute particle suspension contained in a bounded domain accounting for a non-uniform particle distribution and particle inertia, and possibilities for a reduction of computational cost in the simulations of slender elastic fibers moving in a turbulent fluid flow.
We consider the full particle-fluid interface problem given in terms of the Navier-Stokes equations coupled to momentum equations of a small rigid body. By choosing an appropriate asymptotic scaling for the particle-fluid density ratio and using an asymptotic expansion for the solution components, we derive approximations of the original interface problem. The approximate systems differ according to the chosen scaling of the density ratio in their physical behavior allowing the characterization of different inertial regimes.
We extend the asymptotic approach to the case of many particles suspended in a Newtonian fluid. Under specific assumptions for the combination of particle size and particle number, we derive asymptotic approximations of this system. The approximate systems describe the particle motion which allows to use a mean field approach in order to formulate the continuity equation for the particle probability density function. The coupling of the latter with the approximation for the fluid momentum equation then reveals a macroscopic suspension description which accounts for non-uniform particle distributions in space and for small particle inertia.
A slender fiber in a turbulent air flow can be modeled as a stochastic inextensible one-dimensionally parametrized Kirchhoff beam, i.e., by a stochastic partial differential algebraic equation. Its simulations involve the solution of large non-linear systems of equations by Newton's method. In order to decrease the computational time, we explore different methods for the estimation of the solution. Additionally, we apply smoothing techniques to the Wiener Process in order to regularize the stochastic force driving the fiber, exploring their respective impact on the solution and performance. We also explore the applicability of the Wiener chaos expansion as a solution technique for the simulation of the fiber dynamics.
This doctoral thesis includes five studies that deal with the topics work, well-being, and family formation, as well as their interaction. The studies aim to find answers to the following questions: Do workers’ personality traits determine whether they sort into jobs with performance appraisals? Does job insecurity result in lower quality and quantity of sleep? Do public smoking bans affect subjective well-being by changing individuals’ use of leisure time? Can risk preferences help to explain non-traditional family forms? And finally, are differences in out-of-partnership birth rates between East and West Germany driven by cultural characteristics that have evolved in the two separate politico-economic systems? To answer these questions, the following chapters use basic economic subjects such as working conditions, income, and time use, but also employ a range of sociological and psychological concepts such as personality traits and satisfaction measures. Furthermore, all five studies use data from the German Socio-Economic Panel (SOEP), a representative longitudinal panel of private households in Germany, and apply state-of-the-art microeconometric methods. The findings of this doctoral thesis are important for individuals, employers, and policymakers. Workers and employers benefit from knowing the determinants of occupational sorting, as vacancies can be filled more accurately. Moreover, knowing which job-related problems lead to lower well-being and potentially higher sickness absence likely increases efficiency in the workplace. The research on smoking bans and family formation in chapters 4, 5, and 6 is particularly interesting for policymakers. The results on the effects of smoking bans on subjective well-being presented in chapter 4 suggest that the impacts of tobacco control policies could be weighed more carefully. Additionally, understanding why women are willing to take the risks associated with single motherhood can help to improve policies targeting single mothers.
Many combinatorial optimization problems on finite graphs can be formulated as conic convex programs, e.g. the stable set problem, the maximum clique problem or the maximum cut problem. Especially NP-hard problems can be written as copositive programs. In this case the complexity is moved entirely into the copositivity constraint.
Copositive programming is a quite new topic in optimization. It deals with optimization over the so-called copositive cone, a superset of the positive semidefinite cone, where the quadratic form x^T Ax has to be nonnegative for only the nonnegative vectors x. Its dual cone is the cone of completely positive matrices, which includes all matrices that can be decomposed as a sum of nonnegative symmetric vector-vector-products.
The related optimization problems are linear programs with matrix variables and cone constraints.
However, some optimization problems can be formulated as combinatorial problems on infinite graphs. For example, the kissing number problem can be formulated as a stable set problem on a circle.
In this thesis we will discuss how the theory of copositive optimization can be lifted up to infinite dimension. For some special cases we will give applications in combinatorial optimization.
A huge number of clinical studies and meta-analyses have shown that psychotherapy is effective on average. However, not every patient profits from psychotherapy and some patients even deteriorate in treatment. Due to this result and the restricted generalization of clinical studies to clinical practice, a more patient-focused research strategy has emerged. The question whether a particular treatment works for an individual case is the focus of this paradigm. The use of repeated assessments and the feedback of this information to therapists is a major ingredient of patient-focused research. Improving patient outcomes and reducing dropout rates by the use of psychometric feedback seems to be a promising path. Therapists seem to differ in the degree to which they make use of and profit from such feedback systems. This dissertation aims to better understand therapist differences in the context of patient-focused research and the impact of therapists on psychotherapy. Three different studies are included, which focus on different aspects within the field:
Study I (Chapter 5) investigated how therapists use psychometric feedback in their work with patients and how much therapists differ in their usage. Data from 72 therapists treating 648 patients were analyzed. It could be shown that therapists used the psychometric feedback for most of their patients. Substantial variance in the use of feedback (between 27% and 52%) was attributable to therapists. Therapists were more likely to use feedback when they reported being satisfied with the graphical information they received. The results therefore indicated that not only patient characteristics or treatment progress affected the use of feedback.
Study II (Chapter 6) picked up on the idea of analyzing systematic differences in therapists and applied it to the criterion of premature treatment termination (dropout). To answer the question whether therapist effects occur in terms of patients’ dropout rates, data from 707 patients treated by 66 therapists were investigated. It was shown that approximately six percent of variance in dropout rates could be attributed to therapists, even when initial impairment was controlled for. Other predictors of dropout were initial impairment, sex, education, personality styles, and treatment expectations.
Study III (Chapter 7) extends the dissertation by investigating the impact of a transfer from one therapist to another within ongoing treatments. Data from 124 patients who agreed to and experienced a transfer during their treatment were analyzed. A significant drop in patient-rated as well as therapist-rated alliance levels could be observed after a transfer. On average, there seemed to be no difficulties establishing a good therapeutic alliance with the new therapist, although differences between patients were observed. There was no increase in symptom severity due to therapy transfer. Various predictors of alliance and symptom development after transfer were investigated. Impacts on clinical practice were discussed.
Results of the three studies are discussed and general conclusions are drawn. Implications for future research as well as their utility for clinical practice and decision-making are presented.
In this thesis, we aim to study the sampling allocation problem of survey statistics under uncertainty. We know that the stratum specific variances are generally not known precisely and we have no information about the distribution of uncertainty. The cost of interviewing each person in a stratum is also a highly uncertain parameter as sometimes people are unavailable for the interview. We propose robust allocations to deal with the uncertainty in both stratum specific variances and costs. However, in real life situations, we can face such cases when only one of the variances or costs is uncertain. So we propose three different robust formulations representing these different cases. To the best of our knowledge robust allocation in the sampling allocation problem has not been considered so far in any research.
The first robust formulation for linear problems was proposed by Soyster (1973). Bertsimas and Sim (2004) proposed a less conservative robust formulation for linear problems. We study these formulations and extend them for the nonlinear sampling allocation problem. It is very unlikely to happen that all of the stratum specific variances and costs are uncertain. So the robust formulations are in such a way that we can select how many strata are uncertain which we refer to as the level of uncertainty. We prove that an upper bound on the probability of violation of the nonlinear constraints can be calculated before solving the robust optimization problem. We consider various kinds of datasets and compute robust allocations. We perform multiple experiments to check the quality of the robust allocations and compare them with the existing allocation techniques.
In order to investigate the psychobiological consequences of acute stress under laboratory conditions, a wide range of methods for socially evaluative stress induction have been developed. The present dissertation is concerned with evaluating a virtual reality (VR)-based adaptation of one of the most widely used of those methods, the Trier Social Stress Test (TSST). In the three empirical studies collected in this dissertation, we aimed to examine the efficacy and possible areas of application of the adaptation of this well-established psychosocial stressor in a virtual environment. We found that the TSST-VR reliably incites the activation of the major stress effector systems in the human body, albeit in a slightly less pronounced way than the original paradigm. Moreover, the experience of presence is discussed as one potential factor of influence in the origin of the psychophysiological stress response. Lastly, we present a use scenario for the TSST-VR in which we employed the method to investigate the effects of acute stress on emotion recognition performance. We conclude that, due to its advantages concerning versatility, standardization and economic administration, the paradigm harbors enormous potential not only for psychobiological research, but other applications such as clinical practice as well. Future studies should further explore the underlying effect mechanisms of stress in the virtual realm and the implementation of VR-based paradigms in different fields of application.
Competitive analysis is a well known method for analyzing online algorithms.
Two online optimization problems, the scheduling problems and the list accessing problems, are considered in the thesis of Yida Zhu in the respect of this method.
For both problems, several existing online and offline algorithms are studied. Their performances are compared with the performances of corresponding offline optimal algorithms.
In particular, the list accessing algorithm BIT is carefully reviewed.
The classical proof of its worst case performance get simplified by adapting the knowledge about the optimal offline algorithm.
With regard to average case analysis, a new closed formula is developed to determine the performance of BIT on specific class of instances.
All algorithm considered in this thesis are also implemented in Julia.
Their empirical performances are studied and compared with each other directly.
Entrepreneurship has become an essential phenomenon all over the world because it is a major driving force behind the economic growth and development of a country. It is widely accepted that entrepreneurship development in a country creates new jobs, pro-motes healthy competition through innovation, and benefits the social well being of individuals and societies. The policymakers in both developed and developing countries focus on entrepreneurship because it helps to alleviate impediments to economic development and social welfare. Therefore, policymakers and academic researchers consider the promotion of entrepreneurship as essential for the economy and research-based support is needed for further development of entrepreneurship activities.
The impact of entrepreneurial activities on economic and social development also varies from country to country. The effect of entrepreneurial activities on economic and social development also varies from country to country because the level of entrepreneur-ship activities also varies from one region to another or one country to another. To under-stand these variations, policymakers have investigated the determinants of entrepreneur-ship at different levels, such as the individual, industry, and country levels. Moreover, entrepreneurship behavior is influenced by various personal and environmental level factors. However, these personal-level factors cannot be separated from the surrounding environment.
The link between religion and entrepreneurship is well established and can be traced back to Weber (1930). Researchers have analyzed the relationship between religion and entrepreneurship from various perspectives, and the research related to religion and entrepreneurship is diversified and scattered across disciplines. This dissertation tries to explain the link between religion and entrepreneurship, specifically Islamic religion and entrepreneurship. Technically this dissertation comprises three parts. The first part of this dissertation consists of two chapters that discuss the definition and theories of entrepreneurship (Chapter 2) and the theoretical relationship between religion and entrepreneur-ship (Chapter 3).
The second part of this dissertation (Chapter 4) provides an overview of the field with a purpose to gain a better understanding of the field’s current state of knowledge to bridge the different views and perspectives. In order to provide an overview of the field, a systematic literature search leading to a descriptive overview of the field based on 270 articles published in 163 journals Subsequently, bibliometric methods are used to identify thematic clusters, the most influential authors and articles, and how they are connected.
The third part of this dissertation (Chapter 5) empirically evaluates the influence of Islamic values and Islamic religious practices on entrepreneurship intentions within the Islamic community. Using the theory of planned behavior as a theoretical lens, we also take into account that the relationship between religion and entrepreneurial intentions can be mediated by individual’s attitude towards entrepreneurship. A self-administrative questionnaire was used to collect the responses from a sample of 1895 Pakistani university students. A structured equation modeling was adopted to perform a nuanced assessment of the relationship between Islamic values and practices and entrepreneurship intentions and to account for mediating effect of attitude towards entrepreneurship.
The research on religion and entrepreneurship has increased sharply during the last years and is scattered across various academic disciplines and fields. The analysis identifies and characterize the most important publications, journals, and authors in the area and map the analyzed religions and regions. The comprehensive overview of previous studies allows us to identify research gaps and derive avenues for future research in a substantiated way. Moreover, this dissertation helps the research scholars to understand the field in its entirety, identify relevant articles, and to uncover parallels and differences across religions and regions. Besides, the study reveals a lack of empirical research related to specific religions and specific regions. Therefore, scholars can take these regions and religions into consideration when conducting empirical research.
Furthermore, the empirical analysis about the influence of Islamic religious values and Islamic religious practices show that Islamic values served as a guiding principle in shaping people’s attitudes towards entrepreneurship in an Islamic community; they had an indirect influence on entrepreneurship intention through attitude. Similarly, the relationship between Islamic religious practices and the entrepreneurship intentions of students was fully mediated by the attitude towards entrepreneurship. Furthermore, this dissertation contributes to prior research on entrepreneurship in Islamic communities by applying a more fine-grained approach to capture the link between religion and entrepreneurship. Moreover, it contributes to the literature on entrepreneurship intentions by showing that the influence of religion on entrepreneurship intentions is mainly due to religious values and practices, which shape the attitude towards entrepreneurship and thereby influence entrepreneurship intentions in religious communities. The entrepreneur-ship research has put a higher emphasis on assessing the influence of a diverse set of con-textual factors. This dissertation introduces Islamic values and Islamic religious practices as critical contextual factors that shape entrepreneurship in countries that are characterized by the Islamic religion.
We consider a linear regression model for which we assume that some of the observed variables are irrelevant for the prediction. Including the wrong variables in the statistical model can either lead to the problem of having too little information to properly estimate the statistic of interest, or having too much information and consequently describing fictitious connections. This thesis considers discrete optimization to conduct a variable selection. In light of this, the subset selection regression method is analyzed. The approach gained a lot of interest in recent years due to its promising predictive performance. A major challenge associated with the subset selection regression is the computational difficulty. In this thesis, we propose several improvements for the efficiency of the method. Novel bounds on the coefficients of the subset selection regression are developed, which help to tighten the relaxation of the associated mixed-integer program, which relies on a Big-M formulation. Moreover, a novel mixed-integer linear formulation for the subset selection regression based on a bilevel optimization reformulation is proposed. Finally, it is shown that the perspective formulation of the subset selection regression is equivalent to a state-of-the-art binary formulation. We use this insight to develop novel bounds for the subset selection regression problem, which show to be highly effective in combination with the proposed linear formulation.
In the second part of this thesis, we examine the statistical conception of the subset selection regression and conclude that it is misaligned with its intention. The subset selection regression uses the training error to decide on which variables to select. The approach conducts the validation on the training data, which oftentimes is not a good estimate of the prediction error. Hence, it requires a predetermined cardinality bound. Instead, we propose to select variables with respect to the cross-validation value. The process is formulated as a mixed-integer program with the sparsity becoming subject of the optimization. Usually, a cross-validation is used to select the best model out of a few options. With the proposed program the best model out of all possible models is selected. Since the cross-validation is a much better estimate of the prediction error, the model can select the best sparsity itself.
The thesis is concluded with an extensive simulation study which provides evidence that discrete optimization can be used to produce highly valuable predictive models with the cross-validation subset selection regression almost always producing the best results.
This dissertation deals with consistent estimates in household surveys. Household surveys are often drawn via cluster sampling, with households sampled at the first stage and persons selected at the second stage. The collected data provide information for estimation at both the person and the household level. However, consistent estimates are desirable in the sense that the estimated household-level totals should coincide with the estimated totals obtained at the person-level. Current practice in statistical offices is to use integrated weighting. In this approach consistent estimates are guaranteed by equal weights for all persons within a household and the household itself. However, due to the forced equality of weights, the individual patterns of persons are lost and the heterogeneity within households is not taken into account. In order to avoid the negative consequences of integrated weighting, we propose alternative weighting methods in the first part of this dissertation that ensure both consistent estimates and individual person weights within a household. The underlying idea is to limit the consistency conditions to variables that emerge in both the personal and household data sets. These common variables are included in the person- and household-level estimator as additional auxiliary variables. This achieves consistency more directly and only for the relevant variables, rather than indirectly by forcing equal weights on all persons within a household. Further decisive advantages of the proposed alternative weighting methods are that original individual rather than the constructed aggregated auxiliaries are utilized and that the variable selection process is more flexible because different auxiliary variables can be incorporated in the person-level estimator than in the household-level estimator.
In the second part of this dissertation, the variances of a person-level GREG estimator and an integrated estimator are compared in order to quantify the effects of the consistency requirements in the integrated weighting approach. One of the challenges is that the estimators to be compared are of different dimensions. The proposed solution is to decompose the variance of the integrated estimator into the variance of a reduced GREG estimator, whose underlying model is of the same dimensions as the person-level GREG estimator, and add a constructed term that captures the effects disregarded by the reduced model. Subsequently, further fields of application for the derived decomposition are proposed such as the variable selection process in the field of econometrics or survey statistics.
Nonlocal operators are used in a wide variety of models and applications due to many natural phenomena being driven by nonlocal dynamics. Nonlocal operators are integral operators allowing for interactions between two distinct points in space. The nonlocal models investigated in this thesis involve kernels that are assumed to have a finite range of nonlocal interactions. Kernels of this type are used in nonlocal elasticity and convection-diffusion models as well as finance and image analysis. Also within the mathematical theory they arouse great interest, as they are asymptotically related to fractional and classical differential equations.
The results in this thesis can be grouped according to the following three aspects: modeling and analysis, discretization and optimization.
Mathematical models demonstrate their true usefulness when put into numerical practice. For computational purposes, it is important that the support of the kernel is clearly determined. Therefore nonlocal interactions are typically assumed to occur within an Euclidean ball of finite radius. In this thesis we consider more general interaction sets including norm induced balls as special cases and extend established results about well-posedness and asymptotic limits.
The discretization of integral equations is a challenging endeavor. Especially kernels which are truncated by Euclidean balls require carefully designed quadrature rules for the implementation of efficient finite element codes. In this thesis we investigate the computational benefits of polyhedral interaction sets as well as geometrically approximated interaction sets. In addition to that we outline the computational advantages of sufficiently structured problem settings.
Shape optimization methods have been proven useful for identifying interfaces in models governed by partial differential equations. Here we consider a class of shape optimization problems constrained by nonlocal equations which involve interface-dependent kernels. We derive the shape derivative associated to the nonlocal system model and solve the problem by established numerical techniques.
In this thesis, we consider the solution of high-dimensional optimization problems with an underlying low-rank tensor structure. Due to the exponentially increasing computational complexity in the number of dimensions—the so-called curse of dimensionality—they present a considerable computational challenge and become infeasible even for moderate problem sizes.
Multilinear algebra and tensor numerical methods have a wide range of applications in the fields of data science and scientific computing. Due to the typically large problem sizes in practical settings, efficient methods, which exploit low-rank structures, are essential. In this thesis, we consider an application each in both of these fields.
Tensor completion, or imputation of unknown values in partially known multiway data is an important problem, which appears in statistics, mathematical imaging science and data science. Under the assumption of redundancy in the underlying data, this is a well-defined problem and methods of mathematical optimization can be applied to it.
Due to the fact that tensors of fixed rank form a Riemannian submanifold of the ambient high-dimensional tensor space, Riemannian optimization is a natural framework for these problems, which is both mathematically rigorous and computationally efficient.
We present a novel Riemannian trust-region scheme, which compares favourably with the state of the art on selected application cases and outperforms known methods on some test problems.
Optimization problems governed by partial differential equations form an area of scientific computing which has applications in a variety of areas, ranging from physics to financial mathematics. Due to the inherent high dimensionality of optimization problems arising from discretized differential equations, these problems present computational challenges, especially in the case of three or more dimensions. An even more challenging class of optimization problems has operators of integral instead of differential type in the constraint. These operators are nonlocal, and therefore lead to large, dense discrete systems of equations. We present a novel solution method, based on separation of spatial dimensions and provably low-rank approximation of the nonlocal operator. Our approach allows the solution of multidimensional problems with a complexity which is only slightly larger than linear in the univariate grid size; this improves the state of the art for a particular test problem problem by at least two orders of magnitude.
This thesis discusses revue as a significantly inter-cultural genre in the history of global theatre. During the ‘modernisation’ period in Europe, America and Japan, most major urban cities experienced a boom in revue venues and performances. Few studies about revue have yet been done in theatre studies or in urban cultural studies. My thesis will attempt to reevaluate and redefine revue as a highly intercultural theatre genre by using the concept of liminality. In other words, the aim is to examine revue as a genre built on ‘modern composition of betweenness’, bridging seemingly opposing elements, such as the foreign and the domestic, the classic and the innovative, the traditional and the modern, the professional and the amateur, high and low culture, and the feminine and the masculine. The goal is to regard revue as a liminal genre constructed amidst the negotiations between these binaries, existing in a state of constant flux.
The purpose of this approach is to capture revue as a transitory phenomena in five dimensions: conceptual, spatial, temporal, categorical and physical. Over the course of six chapters, this
inter-disciplinary discussion will reveal the reasons why and the ways by which revue came to establish its prominent position in the Japanese theatre industry. The whole structure is also an attempt to provide plausible ways to apply sociological considerations to theatre studies.
Die vorgelegte Dissertation trägt den Titel Regularization Methods for Statistical Modelling in Small Area Estimation. In ihr wird die Verwendung regularisierter Regressionstechniken zur geographisch oder kontextuell hochauflösenden Schätzung aggregatspezifischer Kennzahlen auf Basis kleiner Stichproben studiert. Letzteres wird in der Fachliteratur häufig unter dem Begriff Small Area Estimation betrachtet. Der Kern der Arbeit besteht darin die Effekte von regularisierter Parameterschätzung in Regressionsmodellen, welche gängiger Weise für Small Area Estimation verwendet werden, zu analysieren. Dabei erfolgt die Analyse primär auf theoretischer Ebene, indem die statistischen Eigenschaften dieser Schätzverfahren mathematisch charakterisiert und bewiesen werden. Darüber hinaus werden die Ergebnisse durch numerische Simulationen veranschaulicht, und vor dem Hintergrund empirischer Anwendungen kritisch verortet. Die Dissertation ist in drei Bereiche gegliedert. Jeder Bereich behandelt ein individuelles methodisches Problem im Kontext von Small Area Estimation, welches durch die Verwendung regularisierter Schätzverfahren gelöst werden kann. Im Folgenden wird jedes Problem kurz vorgestellt und im Zuge dessen der Nutzen von Regularisierung erläutert.
Das erste Problem ist Small Area Estimation in der Gegenwart unbeobachteter Messfehler. In Regressionsmodellen werden typischerweise endogene Variablen auf Basis statistisch verwandter exogener Variablen beschrieben. Für eine solche Beschreibung wird ein funktionaler Zusammenhang zwischen den Variablen postuliert, welcher durch ein Set von Modellparametern charakterisiert ist. Dieses Set muss auf Basis von beobachteten Realisationen der jeweiligen Variablen geschätzt werden. Sind die Beobachtungen jedoch durch Messfehler verfälscht, dann liefert der Schätzprozess verzerrte Ergebnisse. Wird anschließend Small Area Estimation betrieben, so sind die geschätzten Kennzahlen nicht verlässlich. In der Fachliteratur existieren hierfür methodische Anpassungen, welche in der Regel aber restriktive Annahmen hinsichtlich der Messfehlerverteilung benötigen. Im Rahmen der Dissertation wird bewiesen, dass Regularisierung in diesem Kontext einer gegen Messfehler robusten Schätzung entspricht - und zwar ungeachtet der Messfehlerverteilung. Diese Äquivalenz wird anschließend verwendet, um robuste Varianten bekannter Small Area Modelle herzuleiten. Für jedes Modell wird ein Algorithmus zur robusten Parameterschätzung konstruiert. Darüber hinaus wird ein neuer Ansatz entwickelt, welcher die Unsicherheit von Small Area Schätzwerten in der Gegenwart unbeobachteter Messfehler quantifiziert. Es wird zusätzlich gezeigt, dass diese Form der robusten Schätzung die wünschenswerte Eigenschaft der statistischen Konsistenz aufweist.
Das zweite Problem ist Small Area Estimation anhand von Datensätzen, welche Hilfsvariablen mit unterschiedlicher Auflösung enthalten. Regressionsmodelle für Small Area Estimation werden normalerweise entweder für personenbezogene Beobachtungen (Unit-Level), oder für aggregatsbezogene Beobachtungen (Area-Level) spezifiziert. Doch vor dem Hintergrund der stetig wachsenden Datenverfügbarkeit gibt es immer häufiger Situationen, in welchen Daten auf beiden Ebenen vorliegen. Dies beinhaltet ein großes Potenzial für Small Area Estimation, da somit neue Multi-Level Modelle mit großem Erklärungsgehalt konstruiert werden können. Allerdings ist die Verbindung der Ebenen aus methodischer Sicht kompliziert. Zentrale Schritte des Inferenzschlusses, wie etwa Variablenselektion und Parameterschätzung, müssen auf beiden Levels gleichzeitig durchgeführt werden. Hierfür existieren in der Fachliteratur kaum allgemein anwendbare Methoden. In der Dissertation wird gezeigt, dass die Verwendung ebenenspezifischer Regularisierungsterme in der Modellierung diese Probleme löst. Es wird ein neuer Algorithmus für stochastischen Gradientenabstieg zur Parameterschätzung entwickelt, welcher die Informationen von allen Ebenen effizient unter adaptiver Regularisierung nutzt. Darüber hinaus werden parametrische Verfahren zur Abschätzung der Unsicherheit für Schätzwerte vorgestellt, welche durch dieses Verfahren erzeugt wurden. Daran anknüpfend wird bewiesen, dass der entwickelte Ansatz bei adäquatem Regularisierungsterm sowohl in der Schätzung als auch in der Variablenselektion konsistent ist.
Das dritte Problem ist Small Area Estimation von Anteilswerten unter starken verteilungsbezogenen Abhängigkeiten innerhalb der Kovariaten. Solche Abhängigkeiten liegen vor, wenn eine exogene Variable durch eine lineare Transformation einer anderen exogenen Variablen darstellbar ist (Multikollinearität). In der Fachliteratur werden hierunter aber auch Situationen verstanden, in welchen mehrere Kovariate stark korreliert sind (Quasi-Multikollinearität). Wird auf einer solchen Datenbasis ein Regressionsmodell spezifiziert, dann können die individuellen Beiträge der exogenen Variablen zur funktionalen Beschreibung der endogenen Variablen nicht identifiziert werden. Die Parameterschätzung ist demnach mit großer Unsicherheit verbunden und resultierende Small Area Schätzwerte sind ungenau. Der Effekt ist besonders stark, wenn die zu modellierende Größe nicht-linear ist, wie etwa ein Anteilswert. Dies rührt daher, dass die zugrundeliegende Likelihood-Funktion nicht mehr geschlossen darstellbar ist und approximiert werden muss. Im Rahmen der Dissertation wird gezeigt, dass die Verwendung einer L2-Regularisierung den Schätzprozess in diesem Kontext signifikant stabilisiert. Am Beispiel von zwei nicht-linearen Small Area Modellen wird ein neuer Algorithmus entwickelt, welche den bereits bekannten Quasi-Likelihood Ansatz (basierend auf der Laplace-Approximation) durch Regularisierung erweitert und verbessert. Zusätzlich werden parametrische Verfahren zur Unsicherheitsmessung für auf diese Weise erhaltene Schätzwerte beschrieben.
Vor dem Hintergrund der theoretischen und numerischen Ergebnisse wird in der Dissertation demonstriert, dass Regularisierungsmethoden eine wertvolle Ergänzung der Fachliteratur für Small Area Estimation darstellen. Die hier entwickelten Verfahren sind robust und vielseitig einsetzbar, was sie zu hilfreichen Werkzeugen der empirischen Datenanalyse macht.
This dissertation investigates corporate acquisition decisions that represent important corporate development activities for family and non-family firms. The main research objective of this dissertation is to generate insights into the subjective decision-making behavior of corporate decision-makers from family and non-family firms and their weighting of M&A decision-criteria during the early pre-acquisition target screening and selection process. The main methodology chosen for the investigation of M&A decision-making preferences and the weighting of M&A decision criteria is a choice-based conjoint analysis. The overall sample of this dissertation consists of 304 decision-makers from 264 private and public family and non-family firms from mainly Germany and the DACH-region. In the first empirical part of the dissertation, the relative importance of strategic, organizational and financial M&A decision-criteria for corporate acquirers in acquisition target screening is investigated. In addition, the author uses a cluster analysis to explore whether distinct decision-making patterns exist in acquisition target screening. In the second empirical part, the dissertation explores whether there are differences in investment preferences in acquisition target screening between family and non-family firms and within the group of family firms. With regards to the heterogeneity of family firms, the dissertation generated insights into how family-firm specific characteristics like family management, the generational stage of the firm and non-economic goals such as transgenerational control intention influences the weighting of different M&A decision criteria in acquisition target screening. The dissertation contributes to strategic management research, in specific to M&A literature, and to family business research. The results of this dissertation generate insights into the weighting of M&A decision-making criteria and facilitate a better understanding of corporate M&A decisions in family and non-family firms. The findings show that decision-making preferences (hence the weighting of M&A decision criteria) are influenced by characteristics of the individual decision-maker, the firm and the environment in which the firm operates.
Hypothalamic-pituitary-adrenal (HPA) axis-related genetic variants influence the stress response
(2019)
The physiological stress system includes the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic-adrenal-medullary system (SAM). Parameters representing these systems such as cortisol, blood pressure or heart rate define the physiological reaction in response to a stressor. The main objective of the studies described in this thesis was to understand the role of the HPA-related genetic factors in these two systems. Genetic factors represent one of the components causing individual variations in physiological stress parameters. Five genes involved in the functioning of the HPA axis regarding stress responses are examined in this thesis. They are: corticotropin-releasing hormone (CRH), the glucocorticoid receptor (GR), the mineralocorticoid receptor (MR), the 5-hydroxytryptamine-transporter-linked polymorphic region (5-HTTLPR) in the serotonin transporter (5-HTT) and the brain-derived neurotrophic factor (BDNF) gene. Two hundred thirty-two healthy participants were genotyped. The influence of genetic factors on physiological parameters, such as post-awakening cortisol and blood pressure was assessed, as well as the influence of genetic factors on stress reactivity in response to a socially evaluated cold pressor test (SeCPT). Three studies tested the HPA-related genes each on three different levels. The first study examined the influences of genotypes and haplotypes of these five genes on physiological as well as psychological stress indicators (Chapter 2). The second study examined the effects of GR variants (genotypes and haplotypes) and promoter methylation level on both the SAM system and the HPA axis stress reactivity (Chapter 3). The third study comprised the characterization of CRH promoter haplotypes in an in-vitro study and the association of the CRH promoter with stress indicators in vivo (Chapter 4).
With two-thirds to three-quarters of all companies, family firms are the most common firm type worldwide and employ around 60 percent of all employees, making them of considerable importance for almost all economies. Despite this high practical relevance, academic research took notice of family firms as intriguing research subjects comparatively late. However, the field of family business research has grown eminently over the past two decades and has established itself as a mature research field with a broad thematic scope. In addition to questions relating to corporate governance, family firm succession and the consideration of entrepreneurial families themselves, researchers mainly focused on the impact of family involvement in firms on their financial performance and firm strategy. This dissertation examines the financial performance and capital structure of family firms in various meta-analytical studies. Meta-analysis is a suitable method for summarizing existing empirical findings of a research field as well as identifying relevant moderators of a relationship of interest.
First, the dissertation examines the question whether family firms show better financial performance than non-family firms. A replication and extension of the study by O’Boyle et al. (2012) based on 1,095 primary studies reveals a slightly better performance of family firms compared to non-family firms. Investigating the moderating impact of methodological choices in primary studies, the results show that outperformance holds mainly for large and publicly listed firms and with regard to accounting-based performance measures. Concerning country culture, family firms show better performance in individualistic countries and countries with a low power distance.
Furthermore, this dissertation investigates the sensitivity of family firm performance with regard to business cycle fluctuations. Family firms show a pro-cyclical performance pattern, i.e. their relative financial performance compared to non-family firms is better in economically good times. This effect is particularly pronounced in Anglo-American countries and emerging markets.
In the next step, a meta-analytic structural equation model (MASEM) is used to examine the market valuation of public family firms. In this model, profitability and firm strategic choices are used as mediators. On the one hand, family firm status itself does not have an impact on firms‘ market value. On the other hand, this study finds a positive indirect effect via higher profitability levels and a negative indirect effect via lower R&D intensity. A split consideration of family ownership and management shows that these two effects are mainly driven by family ownership, while family management results in less diversification and internationalization.
Finally, the dissertation examines the capital structure of public family firms. Univariate meta-analyses indicate on average lower leverage ratios in family firms compared to non-family firms. However, there is significant heterogeneity in mean effect sizes across the 45 countries included in the study. The results of a meta-regression reveal that family firms use leverage strategically to secure their controlling position in the firm. While strong creditor protection leads to lower leverage ratios in family firms, strong shareholder protection has the opposite effect.