On Nonparametric Maximum Likelihood Estimations of Multivariate Distribution Function Based on Interval-Censored Data

被引：4

作者：

Deng, Dianliang ^{[1
]}

Fang, Hong-Bin ^{[2
]}

机构：

[1] Univ Regina, Dept Math & Stat, Regina, SK S4S 0A2, Canada

[2] Univ Maryland, Div Biostat, Greenebaum Canc Ctr, Baltimore, MD 21201 USA

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2009年 / 38卷 / 01期

关键词：

Convergence rate; Self-consistency equation; Strong consistency; ASYMPTOTICALLY OPTIMAL ESTIMATION; JOINT DISTRIBUTION FUNCTION; SMOOTH FUNCTIONALS; GENERALIZED MLE; BIVARIATE;

D O I：

10.1080/03610920802155494

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This article considers the nonparametric maximum likelihood estimator (NPMLE) of a joint distribution function when the multivariate failure times of interest are interval-censored. With different types of interval censoring mechanism, the NPMLE's of the multivariate distribution function are studied and the strong consistency for the NPMLEs is obtained in terms of a self-consistency equation. Furthermore, the convergence rate of the estimator is given, which depends on the types of interval censoring mechanism.

引用

页码：54 / 74

页数：21

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