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Sample surveys are a widely used and cost effective tool to gain information about a population under consideration. Nowadays, there is an increasing demand not only for information on the population level but also on the level of subpopulations. For some of these subpopulations of interest, however, very small subsample sizes might occur such that the application of traditional estimation methods is not expedient. In order to provide reliable information also for those so called small areas, small area estimation (SAE) methods combine auxiliary information and the sample data via a statistical model.
The present thesis deals, among other aspects, with the development of highly flexible and close to reality small area models. For this purpose, the penalized spline method is adequately modified which allows to determine the model parameters via the solution of an unconstrained optimization problem. Due to this optimization framework, the incorporation of shape constraints into the modeling process is achieved in terms of additional linear inequality constraints on the optimization problem. This results in small area estimators that allow for both the utilization of the penalized spline method as a highly flexible modeling technique and the incorporation of arbitrary shape constraints on the underlying P-spline function.
In order to incorporate multiple covariates, a tensor product approach is employed to extend the penalized spline method to multiple input variables. This leads to high-dimensional optimization problems for which naive solution algorithms yield an unjustifiable complexity in terms of runtime and in terms of memory requirements. By exploiting the underlying tensor nature, the present thesis provides adequate computationally efficient solution algorithms for the considered optimization problems and the related memory efficient, i.e. matrix-free, implementations. The crucial point thereby is the (repetitive) application of a matrix-free conjugated gradient method, whose runtime is drastically reduced by a matrx-free multigrid preconditioner.

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.

The economic growth theory analyses which factors affect economic growth and tries to analyze how it can last. A popular neoclassical growth model is the Ramsey-Cass-Koopmans model, which aims to determine how much of its income a nation or an economy should save in order to maximize its welfare. In this thesis, we present and analyze an extended capital accumulation equation of a spatial version of the Ramsey model, balancing diffusive and agglomerative effects. We model the capital mobility in space via a nonlocal diffusion operator which allows for jumps of the capital stock from one location to an other. Moreover, this operator smooths out heterogeneities in the factor distributions slower, which generated a more realistic behavior of capital flows. In addition to that, we introduce an endogenous productivity-production operator which depends on time and on the capital distribution in space. This operator models the technological progress of the economy. The resulting mathematical model is an optimal control problem under a semilinear parabolic integro-differential equation with initial and volume constraints, which are a nonlocal analog to local boundary conditions, and box-constraints on the state and the control variables. In this thesis, we consider this problem on a bounded and unbounded spatial domain, in both cases with a finite time horizon. We derive existence results of weak solutions for the capital accumulation equations in both settings and we proof the existence of a Ramsey equilibrium in the unbounded case. Moreover, we solve the optimal control problem numerically and discuss the results in the economic context.

This doctoral thesis examines intergenerational knowledge, its antecedents as well as how participation in intergenerational knowledge transfer is related to the performance evaluation of employees. To answer these questions, this doctoral thesis builds on a literature review and quantitative research methods. A systematic literature study shows that empirical evidence on intergenerational knowledge transfer is limited. Building on prior literature, effects of various antecedents at the interpersonal and organizational level regarding their effects on intergenerational and intragenerational knowledge transfer are postulated. By questioning 444 trainees and trainers, this doctoral thesis also demonstrates that interpersonal antecedents impact how trainees participate in intergenerational knowledge transfer with their trainers. Thereby, the results of this study provide support that interpersonal antecedents are relevant for intergenerational knowledge transfer, yet, also emphasize the implications attached to the assigned roles in knowledge transfer (i.e., whether one is a trainee or trainer). Moreover, the results of an experimental vignette study reveal that participation in intergenerational knowledge transfer is linked to the performance evaluation of employees, yet, is susceptible to whether the employee is sharing or seeking knowledge. Overall, this doctoral thesis provides insights into this topic by covering a multitude of antecedents of intergenerational knowledge transfer, as well as how participation in intergenerational knowledge transfer may be associated with the performance evaluation of employees.

The dissertation deals with methods to improve design-based and model-assisted estimation techniques for surveys in a finite population framework. The focus is on the development of the statistical methodology as well as their implementation by means of tailor-made numerical optimization strategies. In that regard, the developed methods aim at computing statistics for several potentially conflicting variables of interest at aggregated and disaggregated levels of the population on the basis of one single survey. The work can be divided into two main research questions, which are briefly explained in the following sections.
First, an optimal multivariate allocation method is developed taking into account several stratification levels. This approach results in a multi-objective optimization problem due to the simultaneous consideration of several variables of interest. In preparation for the numerical solution, several scalarization and standardization techniques are presented, which represent the different preferences of potential users. In addition, it is shown that by solving the problem scalarized with a weighted sum for all combinations of weights, the entire Pareto frontier of the original problem can be generated. By exploiting the special structure of the problem, the scalarized problems can be efficiently solved by a semismooth Newton method. In order to apply this numerical method to other scalarization techniques as well, an alternative approach is suggested, which traces the problem back to the weighted sum case. To address regional estimation quality requirements at multiple stratification levels, the potential use of upper bounds for regional variances is integrated into the method. In addition to restrictions on regional estimates, the method enables the consideration of box-constraints for the stratum-specific sample sizes, allowing minimum and maximum stratum-specific sampling fractions to be defined.
In addition to the allocation method, a generalized calibration method is developed, which is supposed to achieve coherent and efficient estimates at different stratification levels. The developed calibration method takes into account a very large number of benchmarks at different stratification levels, which may be obtained from different sources such as registers, paradata or other surveys using different estimation techniques. In order to incorporate the heterogeneous quality and the multitude of benchmarks, a relaxation of selected benchmarks is proposed. In that regard, predefined tolerances are assigned to problematic benchmarks at low aggregation levels in order to avoid an exact fulfillment. In addition, the generalized calibration method allows the use of box-constraints for the correction weights in order to avoid an extremely high variation of the weights. Furthermore, a variance estimation by means of a rescaling bootstrap is presented.
Both developed methods are analyzed and compared with existing methods in extensive simulation studies on the basis of a realistic synthetic data set of all households in Germany. Due to the similar requirements and objectives, both methods can be successively applied to a single survey in order to combine their efficiency advantages. In addition, both methods can be solved in a time-efficient manner using very comparable optimization approaches. These are based on transformations of the optimality conditions. The dimension of the resulting system of equations is ultimately independent of the dimension of the original problem, which enables the application even for very large problem instances.

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.

The harmonic Faber operator
(2018)

P. K. Suetin points out in the beginning of his monograph "Faber Polynomials and Faber Series" that Faber polynomials play an important role in modern approximation theory of a complex variable as they are used in representing analytic functions in simply connected domains, and many theorems on approximation of analytic functions are proved with their help [50]. In 1903, the Faber polynomials were firstly discovered by G. Faber. It was Faber's aim to find a generalisation of Taylor series of holomorphic functions in the open unit disc D in the following way. As any holomorphic function in D has a Taylor series representation f(z)=\sum_{\nu=0}^{\infty}a_{\nu}z^{\nu} (z\in\D) converging locally uniformly inside D, for a simply connected domain G, Faber wanted to determine a system of polynomials (Q_n) such that each function f being holomorphic in G can be expanded into a series
f=\sum_{\nu=0}^{\infty}b_{\nu}Q_{\nu} converging locally uniformly inside G. Having this goal in mind, Faber considered simply connected domains bounded by an analytic Jordan curve. He constructed a system of polynomials (F_n) with this property. These polynomials F_n were named after him as Faber polynomials. In the preface of [50], a detailed summary of results concerning Faber polynomials and results obtained by the aid of them is given. An important application of Faber polynomials is e.g. the transfer of known assertions concerning polynomial approximation of functions belonging to the disc algebra to results of the approximation of functions being continuous on a compact continuum K which contains at least two points and has a connected complement and being holomorphic in the interior of K. In this field, the Faber operator denoted by T turns out to be a powerful tool (for an introduction, see e.g. D. Gaier's monograph). It
assigns a polynomial of degree at most n given in the monomial basis \sum_{\nu=0}^{n}a_{\nu}z^{\nu} with a polynomial of degree at most n given in the basis of Faber polynomials \sum_{\nu=0}^{n}a_{\nu}F_{\nu}. If the Faber operator is continuous with respect to the uniform norms, it has a unique continuous extension to an operator mapping the disc algebra onto the space of functions being continuous on the whole compact continuum and holomorphic in its interior. For all f being element of the disc algebra and all polynomials P, via the obvious estimate for the uniform norms ||T(f)-T(P)||<= ||T|| ||f-P||, it can be seen that the original task of approximating F=T(f) by polynomials is reduced to the polynomial approximation of the function f. Therefore, the question arises under which conditions the Faber operator is continuous and surjective. A fundamental result in this regard was established by J. M. Anderson and J. Clunie who showed that if the compact continuum is bounded by a rectifiable Jordan curve with bounded boundary rotation and free from cusps, then the Faber operator with respect to the uniform norms is a topological isomorphism. Now, let f be a harmonic function in D. Similar as above, we find that f has a uniquely determined representation f=\sum_{\nu=-\infty}^{\infty}a_{\nu}p_{\nu}
converging locally uniformly inside D where p_{n}(z)=z^{n} for n\in\N_{0} and p_{-n}(z)=\overline{z}^{n} for n\in\N}. One may ask whether there is an analogue for harmonic functions on simply connected domains G. Indeed, for a domain G bounded by an analytic Jordan curve, the conjecture that each function f being harmonic in G has a uniquely determined representation f=\sum_{\nu= \infty}^{\infty}b_{\nu}F_{\nu} where F_{-n}(z)=\overline{F_{n}(z\)} for n\inN, converging locally uniformly inside G, holds true. Let now K be a compact continuum containing at least two points and having a connected complement. A main component of this thesis will be the examination of the harmonic Faber operator mapping a harmonic polynomial given in the basis of the harmonic monomials \sum_{\nu=-n}^{n}a_{\nu}p_{\nu} to a harmonic polynomial given as \sum_{\nu=-n}^{n}a_{\nu}F_{\nu}.
If this operator, which is based on an idea of J. Müller, is continuous with respect to the uniform norms, it has a unique continuous extension to an operator mapping the functions being continuous on \partial\D onto the continuous functions on K being
harmonic in the interior of K. Harmonic Faber polynomials and the harmonic Faber operator will be the objects accompanying us throughout
our whole discussion. After having given an overview about notations and certain tools we will use in our consideration in the first chapter, we begin our studies with an introduction to the Faber operator and the harmonic Faber operator. We start modestly and consider domains bounded by an analytic Jordan curve. In Section 2, as a first result, we will show that, for such a domain G, the harmonic Faber operator has a unique continuous extension to an operator mapping the space of the harmonic functions in D onto the space
of the harmonic functions in G, and moreover, the harmonic Faber
operator is an isomorphism with respect to the topologies of locally
uniform convergence. In the further sections of this chapter, we illumine the behaviour of the (harmonic) Faber operator on certain function spaces. In the third chapter, we leave the situation of compact continua bounded by an analytic Jordan curve. Instead we consider closures of domains bounded by Jordan curves having a Dini continuous curvature. With the aid of the concept of compact operators and the Fredholm alternative, we are able to show that the harmonic Faber operator is a topological isomorphism. Since, in particular, the main result of the third chapter holds true for closures K of domains bounded by analytic Jordan curves, we can make use of it to obtain new results concerning the approximation of functions being continuous on K and harmonic in the interior of K by harmonic polynomials. To do so, we develop techniques applied by L. Frerick and J. Müller in [11] and adjust them to our setting. So, we can transfer results about the classic Faber operator to the harmonic Faber operator. In the last chapter, we will use the theory of harmonic Faber polynomials
to solve certain Dirichlet problems in the complex plane. We pursue
two different approaches: First, with a similar philosophy as in [50],
we develop a procedure to compute the coefficients of a series \sum_{\nu=-\infty}^{\infty}c_{\nu}F_{\nu} converging uniformly to the solution of a given Dirichlet problem. Later, we will point out how semi-infinite programming with harmonic Faber polynomials as ansatz functions can be used to get an approximate solution of a given Dirichlet problem. We cover both approaches first from a theoretical point of view before we have a focus on the numerical implementation of concrete examples. As application of the numerical computations, we considerably obtain visualisations of the concerned Dirichlet problems rounding out our discussion about the harmonic Faber polynomials and the harmonic Faber operator.

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.

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.

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.

In the modeling context, non-linearities and uncertainty go hand in hand. In fact, the utility function's curvature determines the degree of risk-aversion. This concept is exploited in the first article of this thesis, which incorporates uncertainty into a small-scale DSGE model. More specifically, this is done by a second-order approximation, while carrying out the derivation in great detail and carefully discussing the more formal aspects. Moreover, the consequences of this method are discussed when calibrating the equilibrium condition. The second article of the thesis considers the essential model part of the first paper and focuses on the (forward-looking) data needed to meet the model's requirements. A large number of uncertainty measures are utilized to explain a possible approximation bias. The last article keeps to the same topic but uses statistical distributions instead of actual data. In addition, theoretical (model) and calibrated (data) parameters are used to produce more general statements. In this way, several relationships are revealed with regard to a biased interpretation of this class of models. In this dissertation, the respective approaches are explained in full detail and also how they build on each other.
In summary, the question remains whether the exact interpretation of model equations should play a role in macroeconomics. If we answer this positively, this work shows to what extent the practical use can lead to biased 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.

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.

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.

This dissertation is dedicated to the analysis of the stabilty of portfolio risk and the impact of European regulation introducing risk based classifications for investment funds.
The first paper examines the relationship between portfolio size and the stability of mutual fund risk measures, presenting evidence for economies of scale in risk management. In a unique sample of 338 fund portfolios we find that the volatility of risk numbers decreases for larger funds. This finding holds for dispersion as well as tail risk measures. Further analyses across asset classes provide evidence for the robustness of the effect for balanced and fixed income portfolios. However, a size effect did not emerge for equity funds, suggesting that equity fund managers simply scale their strategy up as they grow. Analyses conducted on the differences in risk stability between tail risk measures and volatilities reveal that smaller funds show higher discrepancies in that respect. In contrast to the majority of prior studies on the basis of ex-post time series risk numbers, this study contributes to the literature by using ex-ante risk numbers based on the actual assets and de facto portfolio data.
The second paper examines the influence of European legislation regarding risk classification of mutual funds. We conduct analyses on a set of worldwide equity indices and find that a strategy based on the long term volatility as it is imposed by the Synthetic Risk Reward Indicator (SRRI) would lead to substantial variations in exposures ranging from short phases of very high leverage to long periods of under investments that would be required to keep the risk classes. In some cases, funds will be forced to migrate to higher risk classes due to limited means to reduce volatilities after crises events. In other cases they might have to migrate to lower risk classes or increase their leverage to ridiculous amounts. Overall, we find if the SRRI creates a binding mechanism for fund managers, it will create substantial interference with the core investment strategy and may incur substantial deviations from it. Fruthermore due to the forced migrations the SRRI degenerates to a passive indicator.
The third paper examines the impact of this volatility based fund classification on portfolio performance. Using historical data on equity indices we find initially that a strategy based on long term portfolio volatility, as it is imposed by the Synthetic Risk Reward Indicator (SRRI), yields better Sharpe Ratios (SRs) and Buy and Hold Returns (BHRs) for the investment strategies matching the risk classes. Accounting for the Fama-French factors reveals no significant alphas for the vast majority of the strategies. In our simulation study where volatility was modelled through a GJR(1,1) - model we find no significant difference in mean returns, but significantly lower SRs for the volatility based strategies. These results were confirmed in robustness checks using alternative models and timeframes. Overall we present evidence which suggests that neither the higher leverage induced by the SRRI nor the potential protection in downside markets does pay off on a risk adjusted basis.

A basic assumption of standard small area models is that the statistic of interest can be modelled through a linear mixed model with common model parameters for all areas in the study. The model can then be used to stabilize estimation. In some applications, however, there may be different subgroups of areas, with specific relationships between the response variable and auxiliary information. In this case, using a distinct model for each subgroup would be more appropriate than employing one model for all observations. If no suitable natural clustering variable exists, finite mixture regression models may represent a solution that „lets the data decide“ how to partition areas into subgroups. In this framework, a set of two or more different models is specified, and the estimation of subgroup-specific model parameters is performed simultaneously to estimating subgroup identity, or the probability of subgroup identity, for each area. Finite mixture models thus offer a fexible approach to accounting for unobserved heterogeneity. Therefore, in this thesis, finite mixtures of small area models are proposed to account for the existence of latent subgroups of areas in small area estimation. More specifically, it is assumed that the statistic of interest is appropriately modelled by a mixture of K linear mixed models. Both mixtures of standard unit-level and standard area-level models are considered as special cases. The estimation of mixing proportions, area-specific probabilities of subgroup identity and the K sets of model parameters via the EM algorithm for mixtures of mixed models is described. Eventually, a finite mixture small area estimator is formulated as a weighted mean of predictions from model 1 to K, with weights given by the area-specific probabilities of subgroup identity.

External capital plays an important role in financing entrepreneurial ventures, due to limited internal capital sources. An important external capital provider for entrepreneurial ventures are venture capitalists (VCs). VCs worldwide are often confronted with thousands of proposals of entrepreneurial ventures per year and must choose among all of these companies in which to invest. Not only do VCs finance companies at their early stages, but they also finance entrepreneurial companies in their later stages, when companies have secured their first market success. That is why this dissertation focuses on the decision-making behavior of VCs when investing in later-stage ventures. This dissertation uses both qualitative as well as quantitative research methods in order to provide answer to how the decision-making behavior of VCs that invest in later-stage ventures can be described.
Based on qualitative interviews with 19 investment professionals, the first insight gained is that for different stages of venture development, different decision criteria are applied. This is attributed to different risks and goals of ventures at different stages, as well as the different types of information available. These decision criteria in the context of later-stage ventures contrast with results from studies that focus on early-stage ventures. Later-stage ventures possess meaningful information on financials (revenue growth and profitability), the established business model, and existing external investors that is not available for early-stage ventures and therefore constitute new decision criteria for this specific context.
Following this identification of the most relevant decision criteria for investors in the context of later-stage ventures, a conjoint study with 749 participants was carried out to understand the relative importance of decision criteria. The results showed that investors attribute the highest importance to 1) revenue growth, (2) value-added of products/services for customers, and (3) management team track record, demonstrating differences when compared to decision-making studies in the context of early-stage ventures.
Not only do the characteristics of a venture influence the decision to invest, additional indirect factors, such as individual characteristics or characteristics of the investment firm, can influence individual decisions. Relying on cognitive theory, this study investigated the influence of various individual characteristics on screening decisions and found that both investment experience and entrepreneurial experience have an influence on individual decision-making behavior. This study also examined whether goals, incentive structures, resources, and governance of the investment firm influence decision making in the context of later-stage ventures. This study particularly investigated two distinct types of investment firms, family offices and corporate venture capital funds (CVC), which have unique structures, goals, and incentive systems. Additional quantitative analysis showed that family offices put less focus on high-growth firms and whether reputable investors are present. They tend to focus more on the profitability of a later-stage venture in the initial screening. The analysis showed that CVCs place greater importance on product and business model characteristics than other investors. CVCs also favor later-stage ventures with lower revenue growth rates, indicating a preference for less risky investments. The results provide various insights for theory and practice.

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.