In this paper we show that the conditional distribution of perturbed chi-square risks can be approximated by certain distributions including the Gaussian distributions. Our results are of interest for conditional extreme value models and multivariate extremes as shown in three applications
AbstractThe paper deals with random vectors X in Rd,d≥2, possessing the stochastic representation X=...
This thesis presents a sharp approximation of the density of long runs of a random walk conditioned ...
In the present work we study multivariate extreme value theory. Our main focus is on exceedances ove...
In this paper we show that the conditional distribution of perturbed chi-square risks can be approxi...
We prove that the componentwise maximum of an i.i.d. triangular array of chi-square random vectors c...
32 pages, 5 figureInternational audienceLet $(X,Y)$ be a bivariate random vector. The estimation of ...
Let {zeta((k))(m,k) (t), t >= 0}, k > 0 be random processes defined as the differences of two ...
The project focuses on the estimation of the probability distribution of a bivariate random vector g...
This paper studies the supremum of chi-square processes with trend over a threshold-dependent-time h...
AbstractLet ξ(t) be a standard stationary Gaussian process with covariance function r(t), and η(t), ...
Multivariate extreme value theory has proven useful for modeling multivariate data in fields such as...
International audienceWe investigate conditions for the existence of the limiting conditional distri...
International audienceFor a GARCH(1,1) process, we study the large deviation asymptotics at the hori...
In the article the outline of asymptotic theory of extreme values has been intro-duced for the appli...
International audienceIn this work, we focus on some conditional extreme risk measures estimation fo...
AbstractThe paper deals with random vectors X in Rd,d≥2, possessing the stochastic representation X=...
This thesis presents a sharp approximation of the density of long runs of a random walk conditioned ...
In the present work we study multivariate extreme value theory. Our main focus is on exceedances ove...
In this paper we show that the conditional distribution of perturbed chi-square risks can be approxi...
We prove that the componentwise maximum of an i.i.d. triangular array of chi-square random vectors c...
32 pages, 5 figureInternational audienceLet $(X,Y)$ be a bivariate random vector. The estimation of ...
Let {zeta((k))(m,k) (t), t >= 0}, k > 0 be random processes defined as the differences of two ...
The project focuses on the estimation of the probability distribution of a bivariate random vector g...
This paper studies the supremum of chi-square processes with trend over a threshold-dependent-time h...
AbstractLet ξ(t) be a standard stationary Gaussian process with covariance function r(t), and η(t), ...
Multivariate extreme value theory has proven useful for modeling multivariate data in fields such as...
International audienceWe investigate conditions for the existence of the limiting conditional distri...
International audienceFor a GARCH(1,1) process, we study the large deviation asymptotics at the hori...
In the article the outline of asymptotic theory of extreme values has been intro-duced for the appli...
International audienceIn this work, we focus on some conditional extreme risk measures estimation fo...
AbstractThe paper deals with random vectors X in Rd,d≥2, possessing the stochastic representation X=...
This thesis presents a sharp approximation of the density of long runs of a random walk conditioned ...
In the present work we study multivariate extreme value theory. Our main focus is on exceedances ove...
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