The Admissibility of the Kaplan-Meier and other Maximum Likelihood Estimators in the Presence of Censoring
Glen D. Meeden, Malay Ghosh, Cidambi Srinivasan, Stephen B. Vardeman · The Annals of Statistics · 1989
For the nonparametric estimation of a survival function when censoring is present, the Kaplan-Meier estimator is often used. The admissibility of this estimator and other related maximum likelihood estimators is demonstrated. This is done by reducing the problem to one involving just the multinomial distribution and then using the stepwise Bayes technique to prove admissibility.