NONPARAMETRIC-ESTIMATION OF REGRESSION PARAMETERS FROM CENSORED-DATA WITH A DISCRETE COVARIATE

被引：7

作者：

RAHBAR, MH

GARDINER, JC

机构：

[1] UNIV WISCONSIN,DEPT MATH,LA CROSSE,WI 54601

[2] MICHIGAN STATE UNIV,DEPT STAT & PROBABIL,E LANSING,MI 48824

来源：

STATISTICS & PROBABILITY LETTERS | 1995年 / 24卷 / 01期

基金：

美国国家卫生研究院;

关键词：

KAPLAN-MEIER ESTIMATOR; ASYMPTOTIC NORMALITY AND RIGHT CENSORED DATA;

D O I：

10.1016/0167-7152(94)00143-V

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：

A noniterative method of estimation is presented in a simple linear regression model where the independent variable (covariate) assumes only a finite number of values and the dependent variable (response) is randomly right censored. The censoring distribution may depend on the covariate values. The efficiency of our estimator is compared with another noniterative estimator in the literature using simulations. The asymptotic normality of the estimators of the regression parameters are established. In addition, the distribution of estimator of the asymptotic variance is obtained.

引用

页码：13 / 20

页数：8

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