A Large Sample Study of Generalized Maximum Likelihood Estimators from Incomplete Data Via Self-Consistency
研究了从不完全数据中估计分布函数的自一致性估计量，证明了其存在性、一致性和收敛性，并应用于右删失和双删失数据的估计。
Self-consistent estimators for estimating distribution functions from incomplete data are presented. In many cases these estimators are also generalized maximum likelihood estimators. In this paper we discuss the theoretical properties of such estimators: existence, uniform consistency, law of the iterated logarithm, and weak convergence. Applications to the product limit estimator for right-censored data and to the estimator proposed by Turnbull (1974, 1976) for doubly (right- and left-) censored data are also given.