Bayesian Estimation on the Generalized Logistic Distribution under Left Type-II Censoring

Abstract

In this paper, given a left type II censored sample from a generalized logistic distribution, we obtain the Bayes estimators and corresponding risks of the unknown parameter under different asymmetric loss functions, assuming different informative and non-informative priors. Elicitation of hyperparameter through prior predictive approach is also discussed. Also we derive the expression for posterior predictive distributions and the credible Intervals. As an illustration, comparisons of these estimators are made through simulation study as well as real life data example along graphical results. The findings of the study indicate that the Bayes estimation under the gamma prior can be preferred.