Bayesian inference for extreme quantiles of heavy tailed distributions

被引：2

作者：

Farias, Rafael B. A. ^{[1
]}

Montoril, Michel H. ^{[2
]}

Andrade, Jose A. A. ^{[1
]}

机构：

[1] Univ Fed Ceara, Dept Stat & Appl Math, Fortaleza, Ceara, Brazil

[2] Univ Fed Juiz de Fora, Dept Stat, Juiz De Fora, Brazil

来源：

STATISTICS & PROBABILITY LETTERS | 2016年 / 113卷

基金：

巴西圣保罗研究基金会;

关键词：

High quantile; HPD interval; Heavy tail; MODELS;

D O I：

10.1016/j.spl.2016.02.020

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：

We propose a new method for estimating extremes quantiles of a wide class of heavy-tailed distributions. Our proposal makes Bayesian inference on extreme quantiles through High Posterior Density intervals. We evaluate the performance of the proposal by numerical results. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：103 / 107

页数：5

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