On tail index estimation based on multivariate data - Télécom Paris Accéder directement au contenu
Article Dans Une Revue Journal of Nonparametric Statistics Année : 2015

On tail index estimation based on multivariate data

Résumé

This article is devoted to the study of tail index estimation based on i.i.d. multivariate observations, drawn from a standard heavy-tailed distribution, that is, of which Pareto-like marginals share the same tail index. A multivariate central limit theorem for a random vector, whose components correspond to (possibly dependent) Hill estimators of the common tail index α, is established under mild conditions. We introduce the concept of (standard) heavy-tailed random vector of tail index α and show how this limit result can be used in order to build an estimator of α with small asymptotic mean squared error, through a proper convex linear combination of the coordinates. Beyond asymptotic results, simulation experiments illustrating the relevance of the approach promoted are also presented.

Dates et versions

hal-02107496 , version 1 (23-04-2019)

Identifiants

Citer

A. Dematteo, Stéphan Clémençon. On tail index estimation based on multivariate data. Journal of Nonparametric Statistics, 2015, 28 (1), pp.152-176. ⟨10.1080/10485252.2015.1124105⟩. ⟨hal-02107496⟩
30 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More