Estimating covariance matrices using estimating functions in nonparametric and semiparametric regression

被引：0

作者：

Carroll, RJ ^{[1
]}

Iturria, SJ ^{[1
]}

Gutierrez, RG ^{[1
]}

机构：

[1] Texas A&M Univ, College Stn, TX 77843 USA

来源：

SELECTED PROCEEDINGS OF THE SYMPOSIUM ON ESTIMATING FUNCTIONS | 1997年 / 32卷

关键词：

estimating equations; kernel regression; nonparametric regression; plug-in semiparametrics; smoothing;

D O I：

10.1214/lnms/1215455058

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：

We use ideas from estimating function theory to derive new, simply computed consistent covariance matrix estimates in nonparametric regression and in a class of semiparametric problems. Unlike other estimates in the literature, ours do not require auxiliary or additional nonparametric regressions.

引用

页码：399 / 404

页数：6

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