Inference for exponentiated Weibull distribution under constant stress partially accelerated life tests with multiple censored

Constant stress partially accelerated life tests are studied according to exponentiated Weibull distribution.Grounded on multiple censoring, the maximum likelihood estimators are determined in connection with unknown distribution parameters and accelerated factor.The confidence intervals of the unknown parameters and acceleration factor are constructed for large sample size.However, it is not possible to obtain the Bayes estimates in plain form, so we apply a Markov chain Monte Carlo method to deal with this issue, which permits us to create a credible interval of the associated parameters.Finally, based on constant stress partially accelerated life tests scheme with exponentiated Weibull distribution under multiple censoring, the illustrative example and the simulation results are used to investigate the maximum likelihood, and Bayesian estimates of the unknown parameters.