Annals of Statistics
- Ann. Statist.
- Volume 13, Number 4 (1985), 1317-1334.
A Large Sample Study of Generalized Maximum Likelihood Estimators from Incomplete Data Via Self-Consistency
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.
Ann. Statist., Volume 13, Number 4 (1985), 1317-1334.
First available in Project Euclid: 12 April 2007
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Generalized maximum likelihood estimator self-consistency incomplete data implicit function theorem uniform consistency law of the iterated logarithm weak convergence product limit estimator censored data
Tsai, Wei-Yann; Crowley, John. A Large Sample Study of Generalized Maximum Likelihood Estimators from Incomplete Data Via Self-Consistency. Ann. Statist. 13 (1985), no. 4, 1317--1334. doi:10.1214/aos/1176349740. https://projecteuclid.org/euclid.aos/1176349740
- See Correction: Wei-Yann Tsai, John Crowley. Correction: A Large Sample Study of Generalized Maximum Likelihood Estimators from Incomplete Data via Self-Consistency. Ann. Statist., Volume 18, Number 1 (1990), 470--470.