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
Wei-Yann Tsai and John Crowley
Abstract
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.
Article information
Source
Ann. Statist., Volume 13, Number 4 (1985), 1317-1334.
Dates
First available in Project Euclid: 12 April 2007
Permanent link to this document
https://projecteuclid.org/euclid.aos/1176349740
Digital Object Identifier
doi:10.1214/aos/1176349740
Mathematical Reviews number (MathSciNet)
MR811495
Zentralblatt MATH identifier
0611.62038
JSTOR
links.jstor.org
Subjects
Primary: 62E20: Asymptotic distribution theory
Secondary: 62G05: Estimation
Keywords
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
Citation
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
Corrections
- 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.Project Euclid: euclid.aos/1176347514
