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

被引：8
|
作者：
Gardes, L. [3 ]
Guillou, A. [1 ,2 ]
Schorgen, A. [1 ,2 ]
机构：
[1] Univ Strasbourg, F-67084 Strasbourg, France
[2] CNRS, IRMA, UMR 7501, F-67084 Strasbourg, France
[3] INRIA Rhone Alpes, Projet Mistis, F-38334 Montbonnot St Martin, Saint Ismier, France
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2012年 / 142卷 / 06期
关键词：
Heavy-tailed distribution; Covariates; Kernel estimator; Asymptotic normality; NONPARAMETRIC-ESTIMATION; MODELS;
D O I：
10.1016/j.jspi.2012.01.011
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
This paper deals with the estimation of the tail 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. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码：1586 / 1598
页数：13
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