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

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.