Bayesian estimation for topp-leone distribution using progressively censored data and asymmetric loss functions

Abstract

In this study, based on progressive type-II censored data, Bayes estimators of the unknown parameter of the Topp-Leone distribution are derived by using informative and non-informative, priors under square error (symmetric), and linex, general entropy, and precautionary (asymmetric) loss functions. The Bayes estimators cannot be obtained in closed-forms, for this reason, Lindley’s approximation method is used to compute the approximate estimates. The asymptotic confidence and the highest posterior density credible intervals for the unknown parameter are obtained. The performances of the proposed Bayes estimators are compared with the corresponding maximum likelihood estimator for different sample sizes in terms of average estimate and mean squared error through an extensive simulation study. Finally, a real data set is provided to illustrate the results.