New estimators of the extreme value index under random right censoring, for heavy-tailed distributions

被引：0

作者：

Julien Worms

Rym Worms

机构：

[1] Université de Versailles-Saint-Quentin-en-Yvelines,Laboratoire de Mathématiques de Versailles (CNRS UMR 8100)

[2] Université Paris-Est,UPEMLV, UPEC

[3] Laboratoire d’Analyse et de Mathématiques Appliquées (CNRS UMR 8050),undefined

来源：

Extremes | 2014年 / 17卷

关键词：

Extreme value index; Tail inference; Random censoring; Kaplan-Meier integration; 62G32 (Extreme value statistics); 62N02 (Estimation for censored data);

D O I：

暂无

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学科分类号：

摘要：

This paper presents new approaches for the estimation of the extreme value index in the framework of randomly censored samples, based on the ideas of Kaplan-Meier integration and the synthetic data approach of Leurgans (1987). These ideas are developed here in the heavy-tailed case, and lead to modifications of the Hill estimator, for which the consistency is proved under first order conditions. Simulations exhibit good performances of the two approaches, compared to the only existing adaptation of the Hill estimator in this context

引用

页码：337 / 358

页数：21