The contributions of this thesis have mainly a dual purpose: introducing several multivariate statistical methodologies where in the major of the cases only stationary of the random variables is assumed, and also highlight some of the applied problems in risk management where extreme value theory may play a role. Mostly every chapter is selfcontained, they have its own more detailed introduction and short conclusion. / Die Kontributionen von dieser Dissertation haben ein doppeltes Ziel: die Darstellung von vielen multivariaten statistischen Verfahren, wobei in der Mehrheit der Fälle nur Stationarität von den Zufallsvariablen angenommen wurde, und die Anwendungen in Risikomanagement in welchem Extremwerttheorie eine wichtige Rolle spielen könnte. Die Struktur der Arbeit ist eigenständig, mit einer detaillierten Einführung und kurzen Zusammenfassung in jedem Kapitel.

The financial crisis starting in mid-2007 is still affecting us, and with increased regulation banks and institutions are supposed to get more solvent and the industry to become more stable. The Basel Committee is working towards more unified regulation across countries, but the question is how the increased regulation is affecting banks financials. Do profitable banks with a solid capital base have a higher ratio of capital buffer? Looking at banks in 16 OECD countries during the period 1993-2009, with country-level panel-data displayed in two simultaneous equation estimations illustrating how profit and capital buffer has changed during these years, and the relation between them. To get an understanding of how the crisis affected these variables the regressions are also done for a pre-crisis period of 1993-2006. Internal funding variables and other economic control variables are explanatory variables and results show the internal funding variables have a large effect on profit and for capital buffer profit have the largest impact. Results imply that profitable banks with a solid capital base do have a higher ratio of capital buffer. The results coincide with the franchise value theory which is applied in the paper.

Value-at-Risk (VaR) has long been the standard risk measure in financial risk management. However, VaR suffers from critical shortcomings as a risk measure when it comes to quantifying the most severe risks, which was made especially apparent during the financial crisis of 2007–2008. An alternative risk measure addressing the shortcomings of VaR known as expected shortfall (ES) is gaining popularity and is set to replace VaR as the standard measure of financial risk. This thesis introduces how extreme value theory can be applied in estimating ES using an unconditional peaks-over-threshold method. This includes giving an introduction to the theoretical foundations of the method. An application of this method is also performed on five different assets. These assets are chosen to serve as a proxy for the more broad asset classes of equity, fixed income, currencies, commodities and cryptocurrencies. In terms of ES, we find that cryptocurrencies is the riskiest asset and fixed income the safest.

Nous présentons et étudions des méthodes d’apprentissage non-supervisé de phénomènes extrêmes multivariés en grande dimension. Dans le cas où chacune des distributions marginales d’un vecteur aléatoire est à queue lourde, l’étude de son comportement dans les régions extrêmes (i.e. loin de l’origine) ne peut plus se faire via les méthodes usuelles qui supposent une moyenne et une variance finies. La théorie des valeurs extrêmes offre alors un cadre adapté à cette étude, en donnant notamment une base théorique à la réduction de dimension à travers la mesure angulaire. La thèse s’articule autour de deux grandes étapes : - Réduire la dimension du problème en trouvant un résumé de la structure de dépendance dans les régions extrêmes. Cette étape vise en particulier à trouver les sous-groupes de composantes étant susceptible de dépasser un seuil élevé de façon simultané. - Modéliser la mesure angulaire par une densité de mélange qui suit une structure de dépendance déterminée à l’avance. Ces deux étapes permettent notamment de développer des méthodes de classification non-supervisée à travers la construction d’une matrice de similarité pour les points extrêmes. / We present and study unsupervised learning methods of multivariate extreme phenomena in high-dimension. Considering a random vector on which each marginal is heavy-tailed, the study of its behavior in extreme regions is no longer possible via usual methods that involve finite means and variances. Multivariate extreme value theory provides an adapted framework to this study. In particular it gives theoretical basis to dimension reduction through the angular measure. The thesis is divided in two main part: - Reduce the dimension by finding a simplified dependence structure in extreme regions. This step aim at recover subgroups of features that are likely to exceed large thresholds simultaneously. - Model the angular measure with a mixture distribution that follows a predefined dependence structure. These steps allow to develop new clustering methods for extreme points in high dimension.

For many real-world problems, optimization could only be formulated with partial information or subject to uncertainty due to reasons such as data measurement error, model misspecification, or that the formulation depends on the non-stationary future. It thus often requires one to make decisions without knowing the problem's full picture. This dissertation considers the robust optimization framework—a worst-case perspective—to characterize uncertainty as feasible regions and optimize over the worst possible scenarios. Two applications in this worst-case perspective are discussed: stochastic estimation and rare-event simulation. Chapters 2 and 3 discuss a min-max framework to enhance existing estimators for simulation problems that involve a bias-variance tradeoff. Biased stochastic estimators, such as finite-differences for noisy gradient estimation, often contain parameters that need to be properly chosen to balance impacts from the bias and the variance. While the optimal order of these parameters in terms of the simulation budget can be readily established, the precise best values depend on model characteristics that are typically unknown in advance. We introduce a framework to construct new classes of estimators, based on judicious combinations of simulation runs on sequences of tuning parameter values, such that the estimators consistently outperform a given tuning parameter choice in the conventional approach, regardless of the unknown model characteristics. We argue the outperformance via what we call the asymptotic minimax risk ratio, obtained by minimizing the worst-case asymptotic ratio between the mean square errors of our estimators and the conventional one, where the worst case is over any possible values of the model unknowns. In particular, when the minimax ratio is less than 1, the calibrated estimator is guaranteed to perform better asymptotically. We identify this minimax ratio for general classes of weighted estimators and the regimes where this ratio is less than 1. Moreover, we show that the best weighting scheme is characterized by a sum of two components with distinct decay rates. We explain how this arises from bias-variance balancing that combats the adversarial selection of the model constants, which can be analyzed via a tractable reformulation of a non-convex optimization problem. Chapters 4 and 5 discuss extreme event estimation using a distributionally robust optimization framework. Conventional methods for extreme event estimation rely on well-chosen parametric models asymptotically justified from extreme value theory (EVT). These methods, while powerful and theoretically grounded, could however encounter difficult bias-variance tradeoffs that exacerbates especially when data size is too small, deteriorating the reliability of the tail estimation. The chapters study a framework based on the recently surging literature of distributionally robust optimization. This approach can be viewed as a nonparametric alternative to conventional EVT, by imposing general shape belief on the tail instead of parametric assumption and using worst-case optimization as a resolution to handle the nonparametric uncertainty. We explain how this approach bypasses the bias-variance tradeoff in EVT. On the other hand, we face a conservativeness-variance tradeoff which we describe how to tackle. We also demonstrate computational tools for the involved optimization problems and compare our performance with conventional EVT across a range of numerical examples.

It has long been accepted by science education research that science inquiry in the classroom is essential to the development of a deep understanding of the nature of science and the world around us. In an effort to understand the relationship between science inquiry, science process skills, the nature of science and science and engineering fairs, this mixed methods study qualitatively explores teaching strategies of exemplary science and engineering teachers (N=6) who mentored several International Science and Engineering Fair finalists within a 10 year period (2004-2014). The quantitative portion of this research explored the relationship between science fair participation and academic achievement. Using the theoretical framework of modern expectancy-value theory, 5 themes emerged. All believed: 1) there is intrinsic value in science inquiry and science fair; 2) all included strategic engagement opportunities for students; 3) intrinsic value and motivation potentially lead to increased academic aptitude; 4) the benefits of science inquiry and science fair outweigh costs; and 5) there is a link between intrinsic value in science and engineering fair and utility value. Of the schools (N=31) identified for the quantitative study, demographic analysis (gender, ethnicity, socio-economic statics, and size of school) narrowed to 8 treatment schools with one control school indicated no statistical relationship between academic performance on a standardized state science examination and science fair participation. An ad hoc study indicated the standardized testing instrument was not an adequate measurement of the level of inquiry included in a science and engineering fair project. In conclusion, a list comprised of exemplary science and engineering fair suggestions was formulated to include descriptions of similar teaching strategies or issues among the exemplary science and engineering fair teachers with intentions of increasing science inquiry or the nature of science in the classroom through the science and engineering fair framework.

This dissertation proposes new statistical methods for modeling and inference for two specific types of time series: integer-valued data and multivariate nonstationary extreme data. We rely on the class of integer-valued autoregressive (INAR) processes for the former, proposing a novel, flexible and elegant way of modeling count phenomena. As for the latter, we are interested in the human brain and its multi-channel electroencephalogram (EEG) recordings, a natural source of extreme events. Thus, we develop new extreme value theory methods for analyzing such data, whether in modeling the conditional extremal dependence for brain connectivity or clustering extreme brain communities of EEG channels. Regarding integer-valued time series, INAR processes are generally defined by specifying the thinning operator and either the innovations or the marginal distributions. The major limitations of such processes include difficulties deriving the marginal properties and justifying the choice of the thinning operator. To overcome these drawbacks, this dissertation proposes a novel approach for building an INAR model that offers the flexibility to prespecify both marginal and innovation distributions. Thus, the thinning operator is no longer subjectively selected but is rather a direct consequence of the marginal and innovation distributions specified by the modeler. Novel INAR processes are introduced following this perspective; these processes include a model with geometric marginal and innovation distributions (Geo-INAR) and models with bounded innovations. We explore the Geo-INAR model, which is a natural alternative to the classical Poisson INAR model. The Geo-INAR process has interesting stochastic properties, such as MA($\infty$) representation, time reversibility, and closed forms for the $h$-th-order transition probabilities, which enables a natural framework to perform coherent forecasting. In the front of multivariate nonstationary extreme data, the focus lies on multi-channel epilepsy data. Epilepsy is a chronic neurological disorder affecting more than 50 million people globally. An epileptic seizure acts like a temporary shock to the neuronal system, disrupting normal electrical activity in the brain. Epilepsy is frequently diagnosed with EEGs. Current statistical approaches for analyzing EEGs use spectral and coherence analysis, which do not focus on extreme behavior in EEGs (such as bursts in amplitude), neglecting that neuronal oscillations exhibit non-Gaussian heavy-tailed probability distributions. To overcome this limitation, this dissertation proposes new approaches to characterize brain connectivity based on extremal features of EEG signals. Two extreme-valued methods to study alterations in the brain network are proposed. One method is Conex-Connect, a pioneering approach linking the extreme amplitudes of a reference EEG channel with the other channels in the brain network. The other method is Club Exco, which clusters multi-channel EEG data based on a spherical $k$-means procedure applied to the "pseudo-angles," derived from extreme amplitudes of EEG signals. Both methods provide new insights into how the brain network organizes itself during an extreme event, such as an epileptic seizure, in contrast to a baseline state.

The objective of this project is to examine the differences between Moroccan and American students with regards to the cultural influences that led them to pursue an engineering degree. Annually since 2015, a partnership between a university in Morocco and MSU allows senior engineering Moroccan students to study at MSU to obtain their graduate degree in aerospace or mechanical engineering. The roughly equal gender representation in most Moroccan cohorts prompted our research question: “How do students from Morocco and the United States describe the cultural reasons that factored into their choice to pursue an engineering degree?” This exploratory qualitative study is guided by the combined frameworks of Hofstede’s Cultural Dimension (HCD) and Expectancy-Value Theory (EVT). The influence of expectancy, family/social structure, and value are evaluated using EVT and cultural factors are evaluated through HCD. We conducted two phases of semi-structured interviews with senior and graduate Moroccan and American students. This study resulted in the modification of the EVT model to include the three constructs of Collectivism, Religion, and Power Distance Index. It also revealed how EVT’s task values manifest differently across cultures. Results indicate that cultural differences manifest primarily through the “Collectivist” mentality among Moroccans, explaining the gender participation difference between Moroccan and American engineering students.

The monetization model Gacha has spread from its country-of-origin Japan and become a global phenomenon. Genshin Impact, a Chinese Gacha game, is currently one of the highest grossing mobile games in the world. Despite its high revenue, Genshin Impact is free to download and play, all purchases in game are voluntary. A question can then be asked: What aspect of Genshin Impact games makes players spend money? This paper aims to answer that question through qualitative interviews with Genshin Impact players and their reasons for paying for in game items. The results show that players chose to spend money on Genshin Impact because they formed emotional attachment to content, and that the enjoyment of the game justified the money they paid. Further research can be conducted on the difference in opinion between players who pay for in game items and players who do not.

Vehicle velocity distributions are of utmost relevance for the efficiency, safety, and sustainability of road traffic. Yet, due to technical limitations, they are often empirically analyzed using spatiotemporal averages. Here, we instead study a novel set of microscopic traffic data from Dresden comprising 346 million data points with a resolution of one vehicle from 145 detector sites with a particular focus on extreme events and distribution tails. By fitting q-exponential and Generalized Extreme Value distributions to the right flank of the empirical velocity distributions, we establish that their tails universally exhibit a power-law behavior with similar decay exponents. We also find that q-exponentials are best suitable to model the vast extent to which speed limit violations in the data occur. Furthermore, combining velocity and time headway distributions, we obtain estimates for free flow velocities that always exceed average velocities and sometimes even significantly exceed speed limits. Likewise, congestion effects are found to play a very minor, almost negligible role in traffic flow at the detector sites. These results provide insights into the current state of traffic in Dresden, hinting toward potentially necessary policy amendments regarding road design, speed limits, and speeding prosecution. They also reveal the potentials and limitations of the data set at hand and thereby lay the groundwork for further, more detailed traffic analyses.