Abstract : Nonparametric regression quantiles can be obtained by inverting a kernel estimator of the conditional distribution. The asymptotic properties of this estimator are well-known in the case of ordinary quantiles of fixed order. The goal of this paper is to establish the strong consistency of the estimator in case of extreme conditional quantiles. In such a case, the probability of exceeding the quantile tends to zero as the sample size increases, and the extreme conditional quantile is thus located in the distribution tails.
Type de document :
Chapitre d'ouvrage
Elias Ould Said. Functional Statistics and Applications, Springer, pp.59--77, 2015, Contributions to Statistics, 978-3-319-22475-6. <10.1007/978-3-319-22476-3_4>
https://hal.inria.fr/hal-00956351
Contributeur : Stephane Girard <>
Soumis le : mardi 26 août 2014 - 17:02:42
Dernière modification le : jeudi 9 mars 2017 - 01:02:04
Document(s) archivé(s) le : jeudi 27 novembre 2014 - 16:30:33
Stephane Girard, Sana Louhichi. On the strong consistency of the kernel estimator of extreme conditional quantiles. Elias Ould Said. Functional Statistics and Applications, Springer, pp.59--77, 2015, Contributions to Statistics, 978-3-319-22475-6. <10.1007/978-3-319-22476-3_4>. <hal-00956351v2>
