Inference for Weibull Competing Risks Data Under Generalized Progressive Hybrid Censoring

Abstract

A competing risks model is considered under a generalized progressive hybrid censoring. When the latent failure times are Weibull distributed, maximum-likelihood estimates for the unknown model parameters are established where the associated existence and uniqueness are shown. An asymptotic distribution of the maximum-likelihood estimators is used to construct approximate confidence intervals via the observed fisher information matrix. Moreover, Bayes point estimates and the highest probability density credible intervals of unknown parameters are also presented, and the Gibbs sampling technique is used to approximate corresponding estimates. Simulation studies and real-life example are presented for illustration purpose.