On Bayes estimation for mixtures of two Weibull distributions under type I censoring
Abstract A failure population need not be homogeneous. It can be a mixture of two or more distinct subpopulations. In this paper a population which can be divided into two subpopulations each representing a different cause of failure, is considered. The subpopulations each follow a Weibull distribution. Maximum likelihood estimators, Bayes estimators with respect to proper priors, and with respect to improper priors for the population parameters and the reliability function are derived. All analysis is based on samples censored at a predetermined test termination time. A comparison of the Bayes estimation and the maximum likelihood estimation is made through Monte Carlo simulation.