Bayesian estimation of the tail index of a heavy tailed distribution under random censoring

被引：6

作者：

Ameraoui, Abdelkader ^{[1
]}

Boukhetala, Kamal ^{[1
]}

Dupuy, Jean-Francois ^{[2
]}

机构：

[1] USTHB, Fac Math, PoBox 32, Algiers, Algeria

[2] IRMAR INSA Rennes, 20 Ave Buttes Coesmes, F-35708 Rennes 7, France

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2016年 / 104卷

关键词：

Extreme value modeling; Random censoring; Maximum a posteriori estimator; Mean posterior estimator; Asymptotic normality of posterior; Simulations; EXTREME; INFERENCE;

D O I：

10.1016/j.csda.2016.06.009

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Bayesian estimation of the tail index of a heavy-tailed distribution is addressed when data are randomly right-censored. Maximum a posteriori and mean posterior estimators are constructed for various prior distributions of the tail index. Convergence of the posterior distribution of the tail index to a Gaussian distribution is established. Finite sample properties of the proposed estimators are investigated via simulations. Tail index estimation requires selecting an appropriate threshold for constructing relative excesses. A Monte Carlo procedure is proposed for tackling this issue. Finally, the proposed estimators are illustrated on a medical dataset. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：148 / 168

页数：21

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