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

Autor:	Ranjita Pandey, Pulkit Srivastava
Rok vydání:	2022
Předmět:	Statistics and Probability Numerical Analysis Analysis Statistics::Computation
Zdroj:	Journal of Reliability and Statistical Studies.
ISSN:	2229-5666 0974-8024
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::501116a5469389bb57a571428df6b6e8 https://doi.org/10.13052/jrss0974-8024.15110 Zobrazit plný text záznamu