An empirical bayes estimator for the scale parameter of the two‐parameter weibull distribution

Abstract An empirical Bayes estimator is given for the scale parameter in the two‐parameter Weibull distribution. The scale parameter is assumed to vary randomly throughout a sequence of experiments according to a common, but unknown, prior distribution. The shape parameter is assumed to be known, however, it may be different in each experiment. The estimator is obtained by means of a continuous approximation to the unknown prior density function. Results from Monte Carlo simulation are reported which show that the estimator has smaller mean‐squared errors than the usual maximum‐likelihood estimator.