Bayesian Estimation for the Exponentiated Weibull Model via Markov Chain Monte Carlo Simulation

被引：21
|
作者：
Jaheen, Zeinhum F. [1 ]
Al Harbi, Mashail M. [2 ]
机构：
[1] King Abdulaziz Univ, Dept Stat, Jeddah 21589, Saudi Arabia
[2] Umm Al Qura Univ, Dept Math, Appl Sci Coll, Mecca, Saudi Arabia
来源：
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2011年 / 40卷 / 04期
关键词：
Balanced loss function; Bayes estimation; Exponentiated-Weibull model; Generalized order statistics; Markov chain Monte Carlo (MCMC); GENERAL-CLASS; DISTRIBUTIONS; FAMILY; PARAMETERS;
D O I：
10.1080/03610918.2010.546543
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：
Bayesian estimation for the two unknown parameters and the reliability function of the exponentiated Weibull model are obtained based on generalized order statistics. Markov chain Monte Carlo (MCMC) methods are considered to compute the Bayes estimates of the target parameters. Our computations are based on the balanced loss function which contains the symmetric and asymmetric loss functions as special cases. The results have been specialized to the progressively Type-II censored data and upper record values. Comparisons are made between Bayesian and maximum likelihood estimators via Monte Carlo simulation.
引用
收藏
页码：532 / 543
页数：12
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