Inference for accelerated competing failure models from Weibull distribution under Type-I progressive hybrid censoring

Abstract

This paper considers constant-stress accelerated competing failure models under Type-I progressive hybrid censoring with binomial random removals. A Weibull distributed life of test units is assumed for a specific cause and by the Newton–Raphson iteration and asymptotic likelihood theory, the maximum likelihood estimates (MLEs) and asymptotic confidence intervals of the unknown parameters are obtained. Based on the noninformative prior, a Gibbs sampling algorithm using adaptive rejection sampling is presented to obtain Bayesian estimates and the Monte Carlo (MC) method is employed to construct the HPD credible intervals. The simulation results are provided to show that Bayesian estimates perform better than MLEs and the change of the removal probability has a significant effect on MLEs.