Bayesian Estimation of Entropy for Burr Type XII Distribution under Progressive Type-II Censored Data

With the rapid development of statistics, information entropy is proposed as an important indicator used to quantify uncertainty. In this paper, maximum likelihood and Bayesian methods are obtain estimators for a two-parameter Burr type XII distribution under progressive type-II censored data. part estimation, asymptotic confidence intervals calculated. we consider non-informative informative priors respectively, asymmetric symmetric loss functions both adopted. Meanwhile, posterior risk also calculated evaluate performances against different functions. numerical simulation, Lindley approximation Markov chain Monte Carlo method were estimates. turn, highest density credible derived. Finally, average absolute bias mean square error methods, real dataset was selected illustrate feasibility above estimation model.