Bayesian Estimation for Linear Exponential Distribution Under Progressive Type -II Censoring in Constant-Stress Partially-Accelerated Life Tests

In this paper, the estimations of linear exponential distribution parameters and the acceleration factor in constant-stress partially-accelerated life tests based on progressive type -II censoring are considered. Maximum likelihood estimations for the considered parameters are obtained. Observed Fisher information matrix is used to construct asymptotic confidence interval to estimate the model parameters through normal approximation. By using Lindley’s approximation and Markov chain Monte Carlo (MCMC) method, approximate Bayes estimates under loss functions are obtained. Finally, the accuracy of the maximum likelihood estimations and Bayesian estimations for the parameters is investigated through simulation studies