An asymptotic theory for semiparametric generalized least squares estimation in partially linear regression models

被引：8

作者：

Chen, GM ^{[1
]}

You, JH

机构：

[1] Univ Calgary, Dept Math & Stat, Calgary, AB T2N 1N4, Canada

[2] Univ N Carolina, Dept Biostat, Chapel Hill, NC 27599 USA

来源：

STATISTICAL PAPERS | 2005年 / 46卷 / 02期

关键词：

heteroscedasticity; partially linear regression model; semiparametric generalized least squares estimator;

D O I：

10.1007/BF02762967

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：

Consider a partially linear regression model with an unknown vector parameter beta, an unknown function 9((.)), and unknown heteroscedastic error variances. In this paper we develop an asymptotic semiparametric generalized least squares estimation theory under some weak moment conditions. These moment conditions are satisfied by many of the error distributions encountered in practice, and our theory does not require the number of replications to go to infinity.

引用

页码：173 / 193

页数：21

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