Analysis of Type-II hybrid censored competing risks data

Abstract

Kundu and Gupta [22] provided the analysis of Type-I hybrid censored competing risks data, when the lifetime distribution of the competing causes of failures follow exponential distribution. In this paper we consider the analysis of Type-II hybrid censored competing risks data. It is assumed that latent lifetime distributions of the competing causes of failures follow independent exponential distributions with different scale parameters. It is observed that the maximum likelihood estimators of the unknown parameters do not always exist. We propose the modified estimators of the scale parameters, which coincide with the corresponding maximum likelihood estimators when they exist, and asymptotically they are equivalent. We obtain the exact distribution of the proposed estimators. Using the exact distributions of the proposed estimators, associated confidence intervals are obtained. The asymptotic and bootstrap confidence intervals of the unknown parameters are also provided. Further, Bayesian inference of some unknown parametric functions under a very flexible Beta-Gamma prior is considered. Bayes estimators and associated credible intervals of the unknown parameters are obtained using Monte Carlo method. Extensive Monte Carlo simulations are performed to see the effectiveness of the proposed estimators and one real data set has been analyzed for the illustrative purposes. It is observed that the proposed model and the method work quite well for this data set.

Reliability Estimation for Dependent Left-Truncated and Right-Censored Competing Risks Data with Illustrations

“…Inference for competing risks data has attracted wide attention and been discussed by many authors. See, some recent works of Rafiee et al [1], Balakrishnan et al [2], Varghese and Vaidyanatha [3], Koley et al [4], among others. In traditional analysis, failure causes are commonly treated as independent, but such assumption sometimes may be improper due to practical complexity.…”