Classical and Bayesian inference for Burr type-III distribution based on progressive type-II hybrid censored data

The aim of this paper is to discuss the estimation and prediction problems for the Burr type-III distribution under progressive type-II hybrid censored data. We obtained maximum likelihood estimators (MLEs) of unknown parameters using stochastic expectation maximization (SEM) algorithms, and the asymptotic variance–covariance matrix of the MLEs under SEM framework is obtained by Fisher’s information matrix. We provide various Bayes estimators for unknown parameters using Lindley’s approximation method and importance sampling technique from square error, entropy, and linex loss functions. Finally, we analyze a real data set and generate a simulation study to compare the performance of various proposed estimators and predictors under different situations.