Statistical inference for multivariate partially linear regression models

被引：6

作者：

You, Jinhong ^{[1
]}

Zhou, Yong ^{[1
,2
]}

Chen, Gemai ^{[3
,4
]}

机构：

[1] Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China

[2] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China

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

[4] Yunnan Univ Finance & Econ, Sch Math & Stat, Kunming 650221, Peoples R China

来源：

CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE | 2013年 / 41卷 / 01期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Contemporaneous correlation; partially linear regression; profile least squares; semiparametric efficiency; two-stage estimation; MSC 2010: Primary 62H12; secondary; 62A10; NONPARAMETRIC REGRESSION; VARIANCE;

D O I：

10.1002/cjs.11169

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 we study a class of multivariate partially linear regression models. Various estimators for the parametric component and the nonparametric component are constructed and their asymptotic normality established. In particular, we propose an estimator of the contemporaneous correlation among the multiple responses and develop a test for detecting the existence of such contemporaneous correlation without using any nonparametric estimation. The performance of the proposed estimators and test is evaluated through some simulation studies and an analysis of a real data set is used to illustrate the developed methodology. The Canadian Journal of Statistics 41: 122; 2013 (c) 2013 Statistical Society of Canada

引用

页码：1 / 22

页数：22

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