We establish the asymptotic normality of marginal sample quantiles for S-mixing vector stationary processes. S-mixing is a recently introduced and widely applicable notion of dependence. Results of some Monte Carlo simulations are given ; Establecemos la normalidad asintótica de cuantiles...

We propose a quantile--based method to estimate the parameters (i.e. locations, dispersions, co--dispersions and the tail index) of an elliptical distribution, and a battery of tests for model adequacy. The method is suitable for vast dimensions since the estimators for the location vector and...

We establish the asymptotic normality of marginal sample quantiles for S-mixing vector stationary processes. S-mixing is a recently introduced and widely applicable notion of dependence. Results of some Monte Carlo simulations are given

We establish the asymptotic normality of marginal sample quantiles for S-mixing vector stationary processes. S-mixing is a recently introduced and widely applicable notion of dependence. Results of some Monte Carlo simulations are given

We propose two classes of semi-parametric estimators for the tail index of a regular varying elliptical random vector. The first one is based on the distance between a tail probability contour and the observations outside this contour. We denote it as the class of separating estimators. The...

We introduce an inference method based on quantiles matching, which is useful for situations where the density function does not have a closed form - but it is simple to simulate - and/or moments do not exist. Functions of theoretical quantiles, which depend on the parameters of the assumed...

Modeling and understanding multivariate extreme events is challenging, but of great importance in various applications — e.g. in biostatistics, climatology, and finance. The separating Hill estimator can be used in estimating the extreme value index of a heavy tailed multivariate elliptical...

Modeling extreme events is of paramount importance in various areas of science — biostatistics, climatology, finance, geology, and telecommunications, to name a few. Most of these application areas involve multivariate data. Estimation of the extreme value index plays a crucial role in...