Bayesian Estimation for the Two Log-Logistic Models Under Joint Type II Censoring Schemes

被引：0

作者：

Pandey, Ranjita ^{[1
]}

Srivastava, Pulkit ^{[1
]}

机构：

[1] Univ Delhi, Dept Stat, Delhi, India

来源：

JOURNAL OF RELIABILITY AND STATISTICAL STUDIES | 2022年 / 15卷 / 01期

关键词：

Log -logistic model; Bayes estimation; Joint type II censoring scheme; Bayesian credible interval; Markov Chain Monte Carlo; EXACT LIKELIHOOD INFERENCE; EXPONENTIAL POPULATIONS; PARAMETER-ESTIMATION; DISTRIBUTIONS; SAMPLE;

D O I：

10.13052/jrss0974-8024.15110

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 present paper, discusses classical and Bayesian estimation of unknown combined parameters of two different log-logistic models with common shape parameters and different scale parameters under a new type of censoring scheme known as joint type II censoring scheme. Maximum likelihood estimators are derived. Bayes estimates of parameters are proposed under different loss functions. Classical asymptotic confidence intervals along with the Bayesian credible intervals and Highest Posterior Density region are also constructed. Markov Chain Monte Carlo approximation method is used for simulating the theoretic results. Comparative assessment of the classical and the Bayes results are illustrated through a real archived dataset.

引用

页码：229 / 260

页数：32

共 33 条

[1] Exact likelihood inference for two populations from two-parameter exponential distributions under joint Type-II censoring [J].