On estimation and influence diagnostics for log-Birnbaum-Saunders Student-t regression models: Full Bayesian analysis

被引：29

作者：

Cancho, Vicente G. ^{[2
]}

Ortega, Edwin M. M. ^{[1
]}

Paula, Gilberto A. ^{[3
]}

机构：

[1] Univ Sao Paulo, Dept Exact Sci, BR-13418900 Sao Paulo, Brazil

[2] Univ Sao Paulo, Dept Appl Math & Stat, BR-13418900 Sao Paulo, Brazil

[3] Univ Sao Paulo, Dept Stat, BR-13418900 Sao Paulo, Brazil

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2010年 / 140卷 / 09期

基金：

巴西圣保罗研究基金会;

关键词：

Generalized Birnbaum-Saunders distribution; Bayesian inference; Bayesian diagnostic measure; Influential observation; Kullback-Leibler divergence; Sinh-normal distribution; Survival analysis; DISTRIBUTIONS; FAMILY;

D O I：

10.1016/j.jspi.2010.02.017

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：

The purpose of this paper is to develop a Bayesian approach for log-Birnbaum-Saunders Student-t regression models under right-censored survival data. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the considered model. In order to attenuate the influence of the outlying observations on the parameter estimates, we present in this paper Birnbaum-Saunders models in which a Student-t distribution is assumed to explain the cumulative damage. Also, some discussions on the model selection to compare the fitted models are given and case deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback-Leibler divergence. The developed procedures are illustrated with a real data set. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：2486 / 2496

页数：11

共 26 条

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