Inference on unknown parameters of a Burr distribution under hybrid censoring

被引：0
作者：
Manoj Kumar Rastogi
Yogesh Mani Tripathi
机构：
[1] Indian Institute of Technology Patna,Department of Mathematics
来源：
Statistical Papers | 2013年 / 54卷
关键词：
Bayes estimates; EM algorithm; Hybrid type I censoring; Importance sampling; Lindley approximation method; Loss functions; Maximum likelihood estimates; 62F10; 62F15; 62N02;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Based on hybrid censored data, the problem of making statistical inference on parameters of a two parameter Burr Type XII distribution is taken up. The maximum likelihood estimates are developed for the unknown parameters using the EM algorithm. Fisher information matrix is obtained by applying missing value principle and is further utilized for constructing the approximate confidence intervals. Some Bayes estimates and the corresponding highest posterior density intervals of the unknown parameters are also obtained. Lindley’s approximation method and a Markov Chain Monte Carlo (MCMC) technique have been applied to evaluate these Bayes estimates. Further, MCMC samples are utilized to construct the highest posterior density intervals as well. A numerical comparison is made between proposed estimates in terms of their mean square error values and comments are given. Finally, two data sets are analyzed using proposed methods.
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页码：619 / 643
页数：24
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