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

Ranjita Pandey () and Pulkit Srivastava ()
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Ranjita Pandey: University of Delhi
Pulkit Srivastava: University of Delhi

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 6, No 14, 2991 pages

Abstract: Abstract 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.

Keywords: Log-logistics model; Joint progressive censoring; Bayesian inference; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-022-01769-0

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