Inference on Partially Observed Competing Risks Models Using Generalized Type-II Hybrid Censoring Scheme

Abstract

This article investigates inference in a competing risks model where failure causes are partially observed, assuming latent failure times follow Weibull distributions. Inference is derived under a generalized type-II hybrid censoring scheme. The maximum likelihood estimators for model parameters and their associated confidence intervals are discussed. Also, we compute Bayes estimators under both informative and non-informative priors, along with their credible intervals. The performance of all estimators is evaluated through Monte Carlo simulations. Finally, for illustrative purposes, a real-world case is explored.