Estimating the conditional tail index by integrating a kernel conditional quantile estimator

Laurent Gardes 1 Armelle Guillou 2 Antoine Schorgen 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 : This paper deals with the estimation of an extreme value index of a heavy-tailed distribution in the presence of covariates. A class of estimators is proposed in this context and its asymptotic normality established under mild regularity conditions. These estimators are functions of a kernel conditional quantile estimator depending on some tuning parameters. The finite sample properties of our estimators are illustrated on a small simulation study.
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Laurent Gardes, Armelle Guillou, Antoine Schorgen. Estimating the conditional tail index by integrating a kernel conditional quantile estimator. Journal of Statistical Planning and Inference, Elsevier, 2012, 142 (6), pp.1586-1598. ⟨10.1016/j.jspi.2012.01.011⟩. ⟨inria-00578479⟩

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