Robust likelihood inference for regression parameters in partially linear models

被引：1

作者：

Shen, Chung-Wei ^{[2
]}

Tsou, Tsung-Shan ^{[1
,3
,4
]}

Balakrishnan, N. ^{[1
,5
]}

机构：

[1] Natl Cent Univ, Inst Stat, Jhongli 320, Taiwan

[2] Acad Sinica, Inst Stat Sci, Taipei 11529, Taiwan

[3] Natl Cent Univ, Inst Syst Biol & Bioinformat, Jhongli 320, Taiwan

[4] Cathay Gen Hosp, Cathay Med Res Inst, Taipei, Taiwan

[5] McMaster Univ, Hamilton, ON L84 4K1, Canada

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2011年 / 55卷 / 04期

关键词：

Robust likelihood; Generalized additive models; Partially-linear models; ESTIMATOR;

D O I：

10.1016/j.csda.2010.10.025

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A robust likelihood approach is proposed for inference about regression parameters in partially-linear models. More specifically, normality is adopted as the working model and is properly corrected to accomplish the objective. Knowledge about the true underlying random mechanism is not required for the proposed method. Simulations and illustrative examples demonstrate the usefulness of the proposed robust likelihood method, even in irregular situations caused by the components of the nonparametric smooth function in partially-linear models. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：1696 / 1714

页数：19

共 23 条

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[Anonymous], 1982, Matrix Algebra Useful for Statistics

[2]

[Anonymous], 1994, Kernel smoothing

[3] Robust estimates in generalized partially linear models [J].