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

页码：1586 / 1598

页数：13

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