Inference for Weibull competing risks model with partially observed failure causes under generalized progressive hybrid censoring

Abstract

In this paper, a competing risks model is studied when the latent failure times follow Weibull distribution. When the failure times are observed under generalized progressive hybrid censoring and the causes of failure are partially observed, the maximum likelihood estimators of the model parameters are established together with associated existence and uniqueness, and the approximate confidence intervals are constructed based on large sample theory. Bayes estimators and associated credible intervals are obtained under fairly general priors. Moreover, classical and Bayesian inferences are also discussed when there is an order restriction on the scale parameters of the Weibull distributions. Finally, a simulation study and a real data example are presented for illustration.