Local asymptotic normality and asymptotical minimax efficiency of the MLE under random censorship

摘要

Here we study the problems of local asymptotic normality of the parametric family of distri- butions and asymptotic minimax efficient estimators when the observations are subject to right censor- ing. Local asymptotic normality will be established under some mild regularity conditions. A lower bound for local asymptotic minimax risk is given with respect to a bowl-shaped loss function, and fur- thermore a necessary and sufficient condition is given in order to achieve this lower bound. Finally, we show that this lower bound can be attained by the maximum likelihood estimator in the censored case and hence it is local asymptotic minimax efficient.