Inference for exponential competing risks data under generalized progressive hybrid censoring

Abstract

In this paper, a competing risks model based on a generalized progressive hybrid censoring is considered. When the latent lifetime distributions of failure causes are exponential distributed and partially observed, maximum likelihood estimates for unknown parameters are established and the associated asymptotic confidence interval estimates are provided by using approximate theory via the observed Fisher information matrix. Moreover, Bayes point estimates and the highest posterior density credible intervals of unknown parameters are also considered, and the importance sampling procedure is used to approximate corresponding estimates. Finally, a real-life example and simulation study are presented for illustration.