Bayesian Estimation of Marshall Olkin Extended Inverse Weibull Distribution Using MCMC Approach

被引：2
|
作者：
Okasha, Hassan M. [1 ,2 ]
El-Baz, A. H. [3 ]
Basheer, Abdulkareem M. [3 ,4 ]
机构：
[1] King AbdulAziz Univ, Fac Sci, Dept Stat, Jeddah, Saudi Arabia
[2] Al Azhar Univ, Fac Sci, Dept Math, Cairo, Egypt
[3] Damietta Univ, Fac Sci, Dept Math, Dumyat, Egypt
[4] Al Bayda Univ, Al Bayda, Yemen
来源：
JOURNAL OF THE INDIAN SOCIETY FOR PROBABILITY AND STATISTICS | 2020年 / 21卷 / 01期
关键词：
Marshall Olkin extended inverse Weibull; Bayesian estimation; Maximum likelihood estimation; MCMC approach; FAMILY;
D O I：
10.1007/s41096-020-00082-y
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：
In this paper, we invoke a new prospective to discuss the estimation of a three-parameter Marshall Olkin extended inverse Weibull distribution based on Markov Chain Monte Carlo (MCMC) approach. The Bayes estimators under the squared error loss and LINEX loss functions are derived for three parameters. MCMC approach is applied to compute the Bayesian estimation of the unknown parameters. Using a real data application, it is shown that the superior performance of Bayesian estimation.
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页码：247 / 257
页数：11
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