Analysis of Exponential Distribution Under Adaptive Type-I Progressive Hybrid Censored Competing Risks Data

Abstract

            A competing risks model based on exponential distribution is considered under adaptive type-I progressive hybrid censoring scheme. We investigate the maximum likelihood estimation and Bayesian estimation for the distribution parameter. The Bayes estimate of the unknown parameter is obtained based on squared error and LINEX loss functions under the assumption of gamma prior. The asymptotic confidence intervals, the Bayes credible intervals and two parametric bootstrap confidence intervals are also proposed. To evaluate the performance of the estimators, a simulation study is carried out.