Bayesian inference for two log-logistic populations under joint progressive type II censoring schemes

被引：0
作者：
Ranjita Pandey
Pulkit Srivastava
机构：
[1] University of Delhi,Department of Statistics
来源：
International Journal of System Assurance Engineering and Management | 2022年 / 13卷
关键词：
Log-logistics model; Joint progressive censoring; Bayesian inference; Markov chain Monte Carlo;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, we have discussed classical and Bayesian estimation of combined parameters of two different log-logistic models under a new type of censoring scheme known as joint progressive type II censoring scheme considering different scale parameters and common shape parameters. Maximum likelihood estimators are constructed with asymptotic confidence intervals. Then, Bayes estimators of parameters are proposed under different loss functions along with credible intervals and highest posterior density intervals. Markov Chain Monte Carlo approximation method has been used for simulation purpose. A real dataset has also been discussed for illustration.
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页码：2981 / 2991
页数：10
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