Statistical Inference of a Competing Risks Model On Improved Adaptive Type-II Progressive Censored Data

Abstract

A competing risks model is studied under improved adaptive type-II progressive censoring scheme (IAT-II PCS) referring to this phenomena. Two independent competing causes of failure are considered where lifetime of these failures are assumed to follow a certain distribution with unknown parameters. Maximum likelihood estimators (MLEs) of unknown parameters are derived. Asymptotic confidence intervals (ACIs) are also constructed using asymptotic normality property of MLE. Existence and uniqueness properties of MLEs are established. Bayes estimators are obtained with respect to both non-informative and informative priors under different loss functions. Highest posterior density (HPD) credible intervals are calculated. A Monte Carlo simulation study is conducted to compare the performance of the proposed estimates. Some results on estimating unknown parameters under this type of censoring can be found in [4], [1], [2], [3].