Parameter estimation of Burr type-III distribution under generalized progressive hybrid censoring scheme

In this article, a generalised progressive hybrid censoring scheme is used to estimate the lifetime characteristics of the Burr type III distribution. Both classical and Bayesian inferential procedures are developed to estimate the parameters of the considered model. The maximum likelihood estimators and their asymptotic confidence intervals for the parameters in the classical framework are derived. Additionally, Bayes estimators under symmetric and asymmetric loss functions are derived using independent gamma priors. The Markov chain Monte Carlo technique is implemented to compute the posterior expectations. Moreover, the expected total time of the test and the expected number of failures are also computed. Next, a Monte Carlo simulation study is performed to assess the performance of the proposed estimators. Two real datasets are analysed to demonstrate the practical applicability of the study. The results show that both classical and Bayesian inferential procedures perform satisfactorily and that the Bayesian results outperform the traditional classical.