Parameter estimation for the Moore-Bilikam distribution under progressive type-II censoring, with application to failure times

Abstract

ABSTRACT The Moore-Bilikam distribution is convenient for survival analysis. The estimation of its parameters and its reliability function is performed by maximum likelihood, expectation-maximization, stochastic expectation-maximization, and the Bayesian method. The data are progressively censored of type II (samples are removed randomly from the experiment). Simulation shows that the expectation-maximization estimator of the parameter and the Bayesian-shrinkage estimator of the reliability function are the most efficient (with the minimum mean square error) when they are based on the Weibull and the Pareto distributions, which are specific cases of the Moore-Bilikam distribution. Bayesian and maximum likelihood estimations using the Moore-Bilikam distribution under type-II progressive censoring allow for fitting empirical failure times of an insulating fluid between two electrodes and the resistance of single carbon fibers. The associated reliability functions are estimated by each method.