In medical diagnostic studies, the area under the Receiver Operating Characteristic (ROC) curve (AUC) and Youden index are two summary measures widely used in the evaluation of the diagnostic accuracy of a medical test with continuous test results. The first half of this dissertation will highlight ROC analysis including extension of Youden index to the partial Youden index as well as novel confidence interval estimation for AUC and Youden index in the presence of covariates in induced linear regression models. Extensive simulation results show that the proposed methods perform well with small to moderate sized samples. In addition, some real examples will be presented to illustrate the methods. The latter half focuses on the application of empirical likelihood method in economics and finance. Two models draw our attention. The first one is the predictive regression model with independent and identically distributed errors. Some uniform tests have been proposed in the literature without distinguishing whether the predicting variable is stationary or nearly integrated. Here, we extend the empirical likelihood methods in Zhu, Cai and Peng (2014) with independent errors to the case of an AR error process. The proposed new tests do not need to know whether the predicting variable is stationary or nearly integrated, and whether it has a finite variance or an infinite variance. Another model we considered is a GARCH(1,1) sequence or an AR(1) model with ARCH(1) errors. It is known that the observations have a heavy tail and the tail index is determined by an estimating equation. Therefore, one can estimate the tail index by solving the estimating equation with unknown parameters replaced by Quasi Maximum Likelihood Estimation (QMLE), and profile empirical likelihood method can be employed to effectively construct a confidence interval for the tail index. However, this requires that the errors of such a model have at least finite fourth moment to ensure asymptotic normality with n1/2 rate of convergence and Wilk's Theorem. We show that the finite fourth moment can be relaxed by employing some Least Absolute Deviations Estimate (LADE) instead of QMLE for the unknown parameters by noting that the estimating equation for determining the tail index is invariant to a scale transformation of the underlying model. Furthermore, the proposed tail index estimators have a normal limit with n1/2 rate of convergence under minimal moment condition, which may have an infinite fourth moment, and Wilk's theorem holds for the proposed profile empirical likelihood methods. Hence a confidence interval for the tail index can be obtained without estimating any additional quantities such as asymptotic variance.

In the dissertation there is investigated a class of Markov chains defined by iterations of a function possessing a property of asymptotical homogeneity. Two problems are solved: 1) there are established rather general conditions under which the chain has unique stationary distribution; 2) for the chains evolving in a real line there are established conditions under which the stationary distribution of the chain is heavy-tailed. / Disertacijoje tirta Markovo grandinių klasė, kurios iteracijos nusakomos atsitiktinėmis asimptotiškai homogeninėmis funkcijomis, ir išspręsti du uždaviniai: 1) surastos bendros sąlygos, kurios garantuoja vienintelio stacionaraus skirstinio egzistavimą; 2) vienmatėms grandinėms surastos sąlygos, kurioms esant stacionarus skirstinys turi "sunkias" uodegas.

Disertacijoje tirta Markovo grandinių klasė, kurios iteracijos nusakomos atsitiktinėmis asimptotiškai homogeninėmis funkcijomis, ir išspręsti du uždaviniai: 1) surastos bendros sąlygos, kurios garantuoja vienintelio stacionaraus skirstinio egzistavimą; 1) vienmatėms grandinėms surastos sąlygos, kurioms esant stacionarus skirstinys turi "sunkias" uodegas. / In the dissertation there is investigated a class of Markov chains defined by iterations of a function possessing a property of asymptotical homogeneity. Two problems are solved: 1) there are established rather general conditions under which the chain has unique stationary distribution; 2) for the chains evolving in a real line there are established conditions under which the stationary distribution of the chain is heavy-tailed.

Since the financial crisis which started in 2007, the risk awareness in the financial sector is greater than ever. Financial institutions such as banks and insurance companies are heavily regulated in order to create a harmonic and resilient global economic environment. Sufficiently large capital buffers may protect institutions from bankruptcy due to some adverse financial events leading to an undesirable outcome for the company. In many regulatory frameworks, the institutions are obliged to estimate high quantiles of their loss distributions. This is relatively unproblematic when large samples of relevant historical data are available. Serious statistical problems appear when only small samples of relevant data are available. One possible solution would be to pool two or more samples that appear to have the same distribution, in order to create a larger sample. This thesis identifies the advantages and risks of pooling of small samples. For some mixtures of normally distributed samples, with what is considered to be the same variances, the pooled data may indicate heavy tails. Since a finite mixture of normally distributed samples has light tails, this is an example of spurious heavy tails. Even though two samples may appear to have the same distribution function it is not necessarily better to pool the samples in order to obtain a larger sample size with the aim of more accurate quantile estimation. For two normally distributed samples of sizes m and n and standard deviations s and v, we find that when v=s is approximately 2, n+m is less than 100 and m=(m+n) is approximately 0.75, then there is a considerable risk of believing that the two samples have equal variance and that the pooled sample has heavy tails. / Efter den finansiella krisen som hade sin start 2007 har riskmedvetenheten inom den finansiella sektorn ökat. Finansiella institutioner så som banker och försäkringsbolag är noga reglerade och kontrollerade för att skapa en stark och stabil världsekonomi. Genom att banker och försäkringsbolag enligt regelverken måste ha kapitalbuffertar som ska skydda mot konkurser vid oväntade och oönskade händelser skapas en mer harmonisk finansiell marknad. Dessa regelverk som institutionerna måste följa innebär ofta att de ansvariga måste skatta höga kvantiler av institutionens förväntade förlustfunktion. Att skapa en pålitligt modell och sedan skatta höga kvantiler är lätt när det finns mycket relevant data tillgänglig. När det inte finns tillr äckligt med historisk data uppkommer statistiska problem. En lösning på problemet är att poola två eller _era grupper av data som ser ut att komma från samma fördelningsfunktion för att på så sätt skapa en större grupp med historisk data tillgänglig. Detta arbetet går igenom fördelar och risker med att poola data när det inte finns tillräckligt med relevant historisk data för att skapa en pålitlig modell. En viss mix av normalfördelade datagrupper som ser ut att ha samma varians kan uppfattas att komma från tungsvansade fördelningar. Eftersom normalfördelningen inte är en tungsvansad fördelning kan denna missuppfattning skapa problem, detta är ett exempel på falska tunga svansar. Även fast två datagrupper ser ut att komma från samma fördelningsfunktion så är det inte nödvändigtvis bättre att poola dessa grupper för att skapa ett större urval. För två normalfördelade datagrupper med storlekarna m och n och standardavvikelserna s och v, är det farligaste scenariot när v=s är ungefär 2, n+m är mindre än 100 och m=(m+n)är ungefär 0.75. När detta inträffar finns det en signifikant risk att de två datagrupperna ser ut att komma från samma fördelningsfunktion och att den poolade datan innehar tungsvansade egenskaper.

一般而言財務資料具有高峰(High Kurtosis)及厚尾(Heavy Tail)的特性，極值理論(Extreme Value Theorem)即是著重於尾部極端事件發生的機率，描繒出尾部極端值的機率分配，以捕捉財務資料中具厚尾的現象，利用估算尾部指數(Tail Index) α值判斷尾部分配的厚、薄程度。一般在估算α值時均是假設α值是不會隨著時間而變動的穩定值，然而在我們所選取的樣本期間內，可能伴隨著一些重大事件，如金融風暴、或是制度面的改變等，均有可能造成尾部極端值發生機率的增加或減少，因此在其樣本期間所估算的α值不應假設為一不變的常數。本文即是針對亞洲四小龍的匯率資料做”尾部參數是否發生結構變化(Structural Change)”之假設檢定，並且找出發生結構變化的時點。 實証結果發現，在1993~2004年間，亞洲四小龍的匯率報酬率其尾部參數確實有發生結構變化的情形。此結論對於風險管理者而言，必須注意到尾部參數α值應該是一個會隨著時間而改變的值，也就是在估算 值時應該要避開發生結構變化的可能時點，或許應於所要估計的樣本期間先執行尾部參數是否有結構變化的檢定，如此才能更準確的估算α值。

[pt] Valor em Risco -VaR- já é parte das ferramentas habituais que um analista financeiro utiliza para estimar o risco de mercado. Na implementação do VaR é necessário que seja estimados quantis de baixa probabilidade para a distribuição condicional dos retornos dos portfólios. A metodologia tradicional para o cálculo do VaR requer a estimação de um modelo tipo GARCH com distribuição normal. Entretanto, a hipótese de normalidade condicional nem sempre é adequada, principalmente quando se deseja estimar o VaR em períodos atípicos, caracterizados pela ocorrência de eventos extremos. Nesta situações a distribuição condicional deve apresentar excesso de curtose. O uso de distribuições derivadas do Teorema do Valor Extremos -TVE-, conhecidas coletivamente como GEV,associadas aos modelos tipo GARCH, tornou possível o cálculo do VaR nestas situações.Um parâmetro chave nas distribuições da família GEV é o índice de cauda, o qual pode ser estimado através do estimador de Hill. Entretanto este estimador apresenta muita sensibilidade em termos de variância e viés com respeito à fração amostral utilizada na sua estimação. O objetivo principal desta dissertação foi fazer uma comparação entre três métodos de escolha da fração amostral, recentemente sugeridos na literatura: o método bootstrap duplo Danielsson, de Haan, Peng e de Vries 1999, o método threshold Guillou e Hall 2001 e o Hill plot alternativo Drees, de Haan e Resnick 2000. A avaliação dos métodos foi feita através do teste de cobertura condicional de Christoffersen 1998, o qual foi aplicado às séries de retornos dos índices: NASDAQ, NIKKEY,MERVAL e IBOVESPA. Os nossos resultados indicam que os três métodos apresentam aproximadamente o mesmo desempenho, com uma ligeira vantagem dos métodos bootstrap duplo e o threshold sobre o Hill plot alternativo, porque este ultimo tem um componente normativo na determinação do índice de cauda ótimo. / [en] Value at Risk -VaR- is already part of the toolkit of financial analysts assessing market risk. In order to implement VaR it is needed to estimate low quantiles of the portfolio returns distribution. Traditional methodologies combine a normal conditional distribution together with ARCH type models to accomplish this goal. Albeit well succeed in evaluating risk for typical periods, this methodology has not been able to accommodate events that occur with very low probabilities. For these situations one needs conditional distributions with excess of kurtosis. The use of distributions derived from the Extreme Value Theory -EVT-, collectively known as Generalized Extreme Value distribution -GEV-, together with ARCH type models have made it possible to address this problem in a proper framework. A key parameter in the GEV distribution is the tail index, which can be estimated by Hill`s estimator. Hill`s estimator is very sensible, in terms of bias and RMSE, to the sample fraction that is used in its estimation. The objective of this dissertation is to compare three recently suggested methods presented in the statistical literature: the double bootstrap method Danielsson, de Haan, Peng and de Vries 1999,the threshold method Guillou and Hall 2001 and the alternative Hill plot Drees, de Haan and Resnick 2000. The methods have been evaluated with respect to the conditional coverage test of Christoffersen 1998, which has been applied to the following returns series : NASDAQ, NIKKEY, MERVAL e IBOVESPA. Our empirical findings suggests that, overall the three methods have the same performance, with some advantage of the bootstrap and threshold methods over the alternative Hill plot, which has a normative component in the determination of the optimal tail index.

This thesis is divided in four chapters. The two first chapters introduce a parametric quantile-based estimation method of univariate heavy-tailed distributions and elliptical distributions, respectively. If one is interested in estimating the tail index without imposing a parametric form for the entire distribution function, but only on the tail behaviour, we propose a multivariate Hill estimator for elliptical distributions in chapter three. In the first three chapters we assume an independent and identically distributed setting, and so as a first step to a dependent setting, using quantiles, we prove in the last chapter the asymptotic normality of marginal sample quantiles for stationary processes under the S-mixing condition.<p><p><p>The first chapter introduces a quantile- and simulation-based estimation method, which we call the Method of Simulated Quantiles, or simply MSQ. Since it is based on quantiles, it is a moment-free approach. And since it is based on simulations, we do not need closed form expressions of any function that represents the probability law of the process. Thus, it is useful in case the probability density functions has no closed form or/and moments do not exist. It is based on a vector of functions of quantiles. The principle consists in matching functions of theoretical quantiles, which depend on the parameters of the assumed probability law, with those of empirical quantiles, which depend on the data. Since the theoretical functions of quantiles may not have a closed form expression, we rely on simulations.<p><p><p>The second chapter deals with the estimation of the parameters of elliptical distributions by means of a multivariate extension of MSQ. In this chapter we propose inference for vast dimensional elliptical distributions. Estimation is based on quantiles, which always exist regardless of the thickness of the tails, and testing is based on the geometry of the elliptical family. The multivariate extension of MSQ faces the difficulty of constructing a function of quantiles that is informative about the covariation parameters. We show that the interquartile range of a projection of pairwise random variables onto the 45 degree line is very informative about the covariation.<p><p><p>The third chapter consists in constructing a multivariate tail index estimator. In the univariate case, the most popular estimator for the tail exponent is the Hill estimator introduced by Bruce Hill in 1975. The aim of this chapter is to propose an estimator of the tail index in a multivariate context; more precisely, in the case of regularly varying elliptical distributions. Since, for univariate random variables, our estimator boils down to the Hill estimator, we name it after Bruce Hill. Our estimator is based on the distance between an elliptical probability contour and the exceedance observations. <p><p><p>Finally, the fourth chapter investigates the asymptotic behaviour of the marginal sample quantiles for p-dimensional stationary processes and we obtain the asymptotic normality of the empirical quantile vector. We assume that the processes are S-mixing, a recently introduced and widely applicable notion of dependence. A remarkable property of S-mixing is the fact that it doesn't require any higher order moment assumptions to be verified. Since we are interested in quantiles and processes that are probably heavy-tailed, this is of particular interest.<p> / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

Véritable carrefour de problématiques économiques, biologiques, sociologiques, culturelles et sanitaires, l’alimentation suscite de nombreuses polémiques. Dans un contexte où les échanges mondiaux facilitent le transport de denrées alimentaires produites dans des conditions environnementales diverses, où la consommation de masse encourage les stratégies visant à réduire les coûts et maximiser le volume de production (OGM, pesticides, etc.) il devient nécessaire de quantifier les risques sanitaires que de tels procédés engendrent. Notre intérêt se place ici sur l’étude de l’exposition chronique, de l’ordre de l’année, à un ensemble de contaminants dont la nocivité à long terme est d’ores et déjà établie. Les dangers et bénéfices de l’alimentation ne se restreignant pas à l’ingestion ou non de substances toxiques, nous ajoutons à nos objectifs l’étude de certains apports nutritionnels. Nos travaux se centrent ainsi autour de trois axes principaux. Dans un premier temps, nous nous intéressons à l'analyse statistique des très fortes expositions chroniques à une ou plusieurs substances chimiques, en nous basant principalement sur des résultats issus de la théorie des valeurs extrêmes. Nous adaptons ensuite des méthodes d'apprentissage statistique de type ensembles de volume minimum pour l'identification de paniers de consommation réalisant un compromis entre risque toxicologique et bénéfice nutritionnel. Enfin, nous étudions les propriétés asymptotiques d'un certain nombre d'estimateurs permettant d'évaluer les caractéristiques de l'exposition, qui prennent en compte le plan de sondage utilisé pour collecter les données. / At a crossroads of economical, sociological, cultural and sanitary issues, dietary analysis is of major importance for public health institutes. When international trade facilitates the transportation of foodstuffs produced in very different environmental conditions, when conspicuous consumption encourages profitable strategies (GMO, pesticides, etc.), it is necessary to quantify the sanitary risks engendered by such economic behaviors. We are interested in the evaluation of chronic types of exposure (at a yearly scale) to food contaminants, the long-term toxicity of which is already well documented. Because dietary risk and benefit is not limited to the abuse or the avoidance of toxic substances, nutritional intakes are also considered. Our work is thus organized along three main lines of research. We first consider the statistical analysis of very high long-term types of exposure to one or more chemical elements present in the food, adopting approaches in keeping with extreme value theory. Then, we adapt classical techniques borrowed from the statistical learning field concerning minimum volume set estimation in order to identify dietary habits that realize a compromise between toxicological risk and nutritional benefit. Finally, we study the asymptotic properties of a number of statistics that can assess the characteristics of the distribution of individual exposure, which take into account the possible survey scheme from which the data originate.

Dans cette de thèse, nous nous intéressons à la modélisation non paramétrique de données extrêmes spatiales. Nos résultats sont basés sur un cadre principal de la théorie des valeurs extrêmes, permettant ainsi d’englober les lois de type Pareto. Ce cadre permet aujourd’hui d’étendre l’étude des événements extrêmes au cas spatial à condition que les propriétés asymptotiques des estimateurs étudiés vérifient les conditions classiques de la Théorie des Valeurs Extrêmes (TVE) en plus des conditions locales sur la structure des données proprement dites. Dans la littérature, il existe un vaste panorama de modèles d’estimation d’événements extrêmes adaptés aux structures des données pour lesquelles on s’intéresse. Néanmoins, dans le cas de données extrêmes spatiales, hormis les modèles max stables,il n’en existe que peu ou presque pas de modèles qui s’intéressent à l’estimation fonctionnelle de l’indice de queue ou de quantiles extrêmes. Par conséquent, nous étendons les travaux existants sur l’estimation de l’indice de queue et des quantiles dans le cadre de données indépendantes ou temporellement dépendantes. La spécificité des méthodes étudiées réside sur le fait que les résultats asymptotiques des estimateurs prennent en compte la structure de dépendance spatiale des données considérées, ce qui est loin d’être trivial. Cette thèse s’inscrit donc dans le contexte de la statistique spatiale des valeurs extrêmes. Elle y apporte trois contributions principales. • Dans la première contribution de cette thèse permettant d’appréhender l’étude de variables réelles spatiales au cadre des valeurs extrêmes, nous proposons une estimation de l’indice de queue d’une distribution à queue lourde. Notre approche repose sur l’estimateur de Hill (1975). Les propriétés asymptotiques de l’estimateur introduit sont établies lorsque le processus spatial est adéquatement approximé par un processus M−dépendant, linéaire causal ou lorsqu'il satisfait une condition de mélange fort (a-mélange). • Dans la pratique, il est souvent utile de lier la variable d’intérêt Y avec une co-variable X. Dans cette situation, l’indice de queue dépend de la valeur observée x de la co-variable X et sera appelé indice de queue conditionnelle. Dans la plupart des applications, l’indice de queue des valeurs extrêmes n’est pas l’intérêt principal et est utilisé pour estimer par exemple des quantiles extrêmes. La contribution de ce chapitre consiste à adapter l’estimateur de l’indice de queue introduit dans la première partie au cadre conditionnel et d’utiliser ce dernier afin de proposer un estimateur des quantiles conditionnels extrêmes. Nous examinons les modèles dits "à plan fixe" ou "fixed design" qui correspondent à la situation où la variable explicative est déterministe et nous utlisons l’approche de la fenêtre mobile ou "window moving approach" pour capter la co-variable. Nous étudions le comportement asymptotique des estimateurs proposés et donnons des résultats numériques basés sur des données simulées avec le logiciel "R". • Dans la troisième partie de cette thèse, nous étendons les travaux de la deuxième partie au cadre des modèles dits "à plan aléatoire" ou "random design" pour lesquels les données sont des observations spatiales d’un couple (Y,X) de variables aléatoires réelles. Pour ce dernier modèle, nous proposons un estimateur de l’indice de queue lourde en utilisant la méthode des noyaux pour capter la co-variable. Nous utilisons un estimateur de l’indice de queue conditionnelle appartenant à la famille de l’estimateur introduit par Goegebeur et al. (2014b). / In this thesis, we investigate nonparametric modeling of spatial extremes. Our resultsare based on the main result of the theory of extreme values, thereby encompass Paretolaws. This framework allows today to extend the study of extreme events in the spatialcase provided if the asymptotic properties of the proposed estimators satisfy the standardconditions of the Extreme Value Theory (EVT) in addition to the local conditions on thedata structure themselves. In the literature, there exists a vast panorama of extreme events models, which are adapted to the structures of the data of interest. However, in the case ofextreme spatial data, except max-stables models, little or almost no models are interestedin non-parametric estimation of the tail index and/or extreme quantiles. Therefore, weextend existing works on estimating the tail index and quantile under independent ortime-dependent data. The specificity of the methods studied resides in the fact that theasymptotic results of the proposed estimators take into account the spatial dependence structure of the relevant data, which is far from trivial. This thesis is then written in thecontext of spatial statistics of extremes. She makes three main contributions.• In the first contribution of this thesis, we propose a new approach of the estimatorof the tail index of a heavy-tailed distribution within the framework of spatial data. This approach relies on the estimator of Hill (1975). The asymptotic properties of the estimator introduced are established when the spatial process is adequately approximated by aspatial M−dependent process, spatial linear causal process or when the process satisfies a strong mixing condition.• In practice, it is often useful to link the variable of interest Y with covariate X. Inthis situation, the tail index depends on the observed value x of the covariate X and theunknown fonction (.) will be called conditional tail index. In most applications, the tailindexof an extreme value is not the main attraction, but it is used to estimate for instance extreme quantiles. The contribution of this chapter is to adapt the estimator of the tail index introduced in the first part in the conditional framework and use it to propose an estimator of conditional extreme quantiles. We examine the models called "fixed design"which corresponds to the situation where the explanatory variable is deterministic. To tackle the covariate, since it is deterministic, we use the window moving approach. Westudy the asymptotic behavior of the estimators proposed and some numerical resultsusing simulated data with the software "R".• In the third part of this thesis, we extend the work of the second part of the framemodels called "random design" for which the data are spatial observations of a pair (Y,X) of real random variables . In this last model, we propose an estimator of heavy tail-indexusing the kernel method to tackle the covariate. We use an estimator of the conditional tail index belonging to the family of the estimators introduced by Goegebeur et al. (2014b).

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