Bayesian and Non Bayesian Estimations for Birnbaum-Saunders Distribution under Partially Accelerated Life Testing Based on Censoring Sampling

In this paper, the maximum likelihood and Bayes estimators for the Constant-stress partially accelerated life testing are considered when the lifetimes of test units are assumed to follow the two-parameter Birnbaum-Saunders distribution. Based on type-II censored samples, the maximum likelihood estimates (MLEs) for the parameters of the model are derived. simulation study is carried out to investigate the precision of the MLEs for the parameters involved. Bayesian estimation have been considered using reference prior with partial information for the parameters of the model under squared error loss function. Due to the complexity of the model, Markovchain Monte Carlo using Gibbs sampling are used to develop a Bayesian estimation for Constant-stress partially accelerated life testing model.