Statistical analysis of double stress accelerated life testing under adaptive type-Ⅱ progressive censoring

Abstract

Aiming at the traditional accelerated life test only considering the single stress and single failure cause, the inference method of double stress accelerated competing risks data is studied under the adaptive type-Ⅱ progressively censored and the life of test units follow Weibull distribution. The maximum likelihood estimations and the asymptotic confidence intervals of unknown parameters are obtained by using the Newton-Raphson iteration and asymptotic likelihood theory. A Gibbs sampling combining with Metropolis-Hasting algorithm method is developed to obtain Bayes estimations, and the Monte Carlo method is employed to construct the HPD credible intervals. The simulated results show that the present method has good statistical inference performance, meanwhile the failure sample size and the expected experimentation time have significant effect on the estimations.