Extreme quantile regression in a proportional tail framework
Résumé
The model of heteroscedastic extremes initially introduced by Einmahl et al. (JRSSB, 2016) describes the evolution of a non stationary sequence whose extremes evolve over time. We revisit this model and adapt it into a general extreme quantile regression framework. We provide estimates for the extreme value index and the integrated skedasis function and prove their joint asymptotic normality. Our results are quite similar to those developed for heteroscedastic extremes but with a different proof approach emphasizing coupling arguments. We also propose a pointwise estimator of the skedasis function and a Weissman estimator of conditional extreme quantiles and prove the asymptotic normality of both estimators.
Domaines
Statistiques [math.ST]Origine | Fichiers produits par l'(les) auteur(s) |
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