Classical and Bayesian Estimation for Two Exponential Populations based on Joint Type-I Progressive Hybrid Censoring Scheme

SYNOPTIC ABSTRACT: Analysis of progressively censored data has received considerable attention in the last few years. In this article, we introduce a new joint type-I progressively hybrid censoring (JPHC-I) scheme. It is assumed that the lifetime distribution of the items from the two populations follow exponential distribution with the same scale parameters. Based on the joint type-I progressive hybrid censoring scheme, we first consider the maximum likelihood estimators of the unknown parameters along with its asymptotic confidence intervals. Next, we provide the Bayesian inferences of the unknown parameters under the assumptions of independent gamma priors on the scale parameters using squared error, linear-exponential (LINEX), and general entropy (GE) loss functions. Monte Carlo simulations are performed to observe the performances of the different estimators and the associated confidence and credible intervals. One real data set has been analyzed for illustrative purposes. © 2019, © 2019 Taylor & Francis Group, LLC.