Empirical likelihood for partial linear models

被引：57

作者：

Wang, QH ^{[1
]}

Jing, BY

机构：

[1] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R China

[2] Heilongjiang Univ, Harbin, Peoples R China

[3] Hong Kong Univ Sci & Technol, Dept Math, Kowloon, Hong Kong, Peoples R China

来源：

ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS | 2003年 / 55卷 / 03期

关键词：

empirical likelihood; partial linear model; Wilks' theorem;

D O I：

10.1007/BF02517809

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：

In this paper the empirical likelihood method due to Owen (1988, Biometrika, 75, 237-249) is applied to partial linear random models. A nonparametric version of Wilks' theorem is derived. The theorem is then used to construct confidence regions of the parameter vector in the partial linear models, which has correct asymptotic coverage. A simulation study is conducted to compare the empirical likelihood and normal approximation based method.

引用

页码：585 / 595

页数：11

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