Nonparametric regression estimation of conditional tails: the random covariate case

Yuri Goegebeur (1) , Armelle Guillou (2) , Antoine Schorgen (2)

1 IMADA - Department of Mathematics and Computer Science [Odense]
2 IRMA - Institut de Recherche Mathématique Avancée

We present families of nonparametric estimators for the conditional tail index of a Pareto-type distribution in the presence of random covariates. These families are constructed from locally weighted sums of power transformations of excesses over a high threshold. The asymptotic properties of the proposed estimators are derived under some assumptions on the conditional response distribution, the weight function and the density function of the covariates. We also introduce bias-corrected versions of the estimators for the conditional tail index, and propose in this context a consistent estimator for the second-order tail parameter. The finite sample performance of some specific examples from our classes of estimators is illustrated with a small simulation experiment.

Mots clés: tail index; Pareto-type distribution; regression; kernel statistic; bias-correction

Domaines: Statistiques [math.ST]

Yuri Goegebeur, Armelle Guillou, Antoine Schorgen. Nonparametric regression estimation of conditional tails: the random covariate case. Statistics, 2014, 48 (4), ⟨10.1080/02331888.2013.800064⟩. ⟨hal-01312934⟩