Aucunhis thesis is a contribution to the statistical modeling of the index of extreme values in the univariate framework. It aims to propose a generalization of Hill's estimator (1975). For this, we are interested in the study of a new functional statistics.This stochastic process Tn (f, s) is such that each of its margins is an estimator of the extremal index (PSMEI). It allows to study the consecutive spacings of the order statistics of the random variable Y = logX.The asymptotic theory of this PSMEI process reveals Gaussian and non-Gaussian boundary laws. The study of the optimal variance of the family of Gaussian estimators allowed us to discover an efficient and very stable estimator of the extremal index when the two parameters f an...
AbstractThe structure of the large values attained by a stationary random process indexed by a one-d...
Let X1, X2,... be a sequence of independent copies (s.i.c) of a real random variable (r.v.) X ≥ 1, w...
Let X1,X2,... be a sequence of independent copies (s.i.c) of a real random variable (r.v.) X> 1, ...
This thesis is divided into five chapters with an additional introduction and a conclusion. In the f...
Generalised autoregressive conditional heteroskedastic (GARCH) processes have wide application in fi...
We propose a new threshold selection method for nonparametric estimation of the extremal index of st...
Title: Stochastical inference in the model of extreme events Author: Jan Dienstbier Department/Insti...
The extremal index (θ) is the key parameter for extending extreme value theory results from IID to s...
The extremal index (θ) is the key parameter for extending extreme value theory results from i.i.d. t...
Extremes Values, Regular Variation and Point Processes is a readable and efficient account of the fu...
Consider n i.i.d. random elements on C[0; 1].We show that under an appropriate strengthening of the ...
Several models with conditional heterosckedasticity have been studied in financial econometrics, wit...
Consider a centered separable Gaussian process $Y$ with a variance function that is regularly varyin...
In this paper we analyze the asymptotic properties of the popular distribution tail index estimator ...
Abstract. This paper considers extreme values attained by a centered, multidimen-sional Gaussian pro...
AbstractThe structure of the large values attained by a stationary random process indexed by a one-d...
Let X1, X2,... be a sequence of independent copies (s.i.c) of a real random variable (r.v.) X ≥ 1, w...
Let X1,X2,... be a sequence of independent copies (s.i.c) of a real random variable (r.v.) X> 1, ...
This thesis is divided into five chapters with an additional introduction and a conclusion. In the f...
Generalised autoregressive conditional heteroskedastic (GARCH) processes have wide application in fi...
We propose a new threshold selection method for nonparametric estimation of the extremal index of st...
Title: Stochastical inference in the model of extreme events Author: Jan Dienstbier Department/Insti...
The extremal index (θ) is the key parameter for extending extreme value theory results from IID to s...
The extremal index (θ) is the key parameter for extending extreme value theory results from i.i.d. t...
Extremes Values, Regular Variation and Point Processes is a readable and efficient account of the fu...
Consider n i.i.d. random elements on C[0; 1].We show that under an appropriate strengthening of the ...
Several models with conditional heterosckedasticity have been studied in financial econometrics, wit...
Consider a centered separable Gaussian process $Y$ with a variance function that is regularly varyin...
In this paper we analyze the asymptotic properties of the popular distribution tail index estimator ...
Abstract. This paper considers extreme values attained by a centered, multidimen-sional Gaussian pro...
AbstractThe structure of the large values attained by a stationary random process indexed by a one-d...
Let X1, X2,... be a sequence of independent copies (s.i.c) of a real random variable (r.v.) X ≥ 1, w...
Let X1,X2,... be a sequence of independent copies (s.i.c) of a real random variable (r.v.) X> 1, ...