Extreme geometric quantiles

Stephane Girard 1 Gilles Stupfler 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : A popular way to study the tail of a distribution is to consider its extreme quantiles. While this is a standard procedure for univariate distributions, it is harder for multivariate ones, primarily because there is no universally accepted definition of what a multivariate quantile should be. We focus on extreme geometric quantiles. We discuss their asymptotics, both in direction and magnitude, when the norm of the associated index vector tends to one. In particular, it appears that if a random vector X has a finite covariance matrix M, then the magnitude of its extreme geometric quantiles grows at a fixed rate and is asymptotically characterised by M. The case when X does not have a finite covariance matrix is tackled in a multivariate regular variation framework. The results are illustrated on simulated data.
Type de document :
Communication dans un congrès
7th International Conference of the ERCIM WG on Computing and Statistics, Dec 2014, Pise, Italy
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01093048
Contributeur : Stephane Girard <>
Soumis le : mercredi 10 décembre 2014 - 09:26:16
Dernière modification le : mardi 31 janvier 2017 - 16:51:32

Identifiants

  • HAL Id : hal-01093048, version 1

Citation

Stephane Girard, Gilles Stupfler. Extreme geometric quantiles. 7th International Conference of the ERCIM WG on Computing and Statistics, Dec 2014, Pise, Italy. <hal-01093048>

Partager

Métriques

Consultations de la notice

160