BAYES ESTIMATION IN EXPONENTIAL CENSORED SAMPLES WITH INCOMPLETE INFORMATION

A Bayesian procedure is proposed to estimate the exponential mean lifetime and the reliability function in a time censored sampling with incomplete information. On the basis of a Monte Carlo study, the Bayes point and interval estimators are compared to the maximum likelihood ones, taking into account several factors, such as prior information, sample size, and censoring time. It is found that only a vague (from an engineering viewpoint) prior knowledge on the mean lifetime is required to make attractive the Bayesian procedure. © 1990, Taylor & Francis Group, LLC. All rights reserved.