Empirical-Bayes Estimation of Mean Life for a Censored Sample, Constant Hazard Rate Model

The estimation of mean life based on a censored sample from a population with a constant failure rate is considered in an empirical Bayes situation. Bayes risks of empirical Bayes estimates for various numbers of past samples are compared with the Bayes risk of the sample mean, and the minimum Bayes risk. A Monte Carlo simulation shows that the Bayes risk of the empirical Bayes estimator is very close to the minimum Bayes risk for as few as 5 past estimates of the mean life. Morever, it is better than the uniformly minimum variance unbiased estimator (the sample mean) in terms of the Bayes risk. Effects of misclassification of the prior are also considered.