Classical and Bayesian Inferences in Step-Stress Partially Accelerated Life Tests for Inverse Weibull Distribution Under Type-I Censoring

This paper deals with the classical and Bayesian estimations of step-stress partially accelerated life test model under type-I censoring for the inverse Weibull lifetime distribution. In classical estimation, the maximum likelihood estimates of the distribution parameters and the acceleration factor were obtained. In addition, approximate confidence intervals of the parameters were constructed based on the asymptotic distribution of the maximum likelihood estimators. Under Bayesian inference, besides the Lindley and Tierney–Kadane approximation posterior expectation methods, which yielded point estimates of the distribution parameters and the acceleration factors under square error loss function, we also applied the Gibbs sampling method, in order to construct credible intervals of these parameters together with their point estimates. Finally, Monte Carlo simulations were conducted to compare the performances of the above estimation methods.