Nonparametric estimation of the conditional tail index

Laurent Gardes 1 Stephane Girard 1
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 : We present a nonparametric family of estimators for the tail index of a Pareto-type distribution when covariate information is available. Our estimators are based on a weighted sum of the log-spacings between some selected observations. This selection is achieved through a moving window approach on the covariate domain and a random threshold on the variable of interest. Asymptotic normality is proved under mild regularity conditions and illustrated for some weight functions. Finite sample performances are presented on a real data study.
Type de document :
Communication dans un congrès
Statistical Extremes and Environmental Risk Workshop, Feb 2007, Lisbonne, Portugal. pp.47-50, 2007
Liste complète des métadonnées

Littérature citée [13 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-00987250
Contributeur : Stephane Girard <>
Soumis le : lundi 5 mai 2014 - 17:34:56
Dernière modification le : mercredi 11 avril 2018 - 01:59:11
Document(s) archivé(s) le : mardi 5 août 2014 - 12:55:45

Identifiants

  • HAL Id : hal-00987250, version 1

Collections

Citation

Laurent Gardes, Stephane Girard. Nonparametric estimation of the conditional tail index. Statistical Extremes and Environmental Risk Workshop, Feb 2007, Lisbonne, Portugal. pp.47-50, 2007. 〈hal-00987250〉

Partager

Métriques

Consultations de la notice

272

Téléchargements de fichiers

170