Statistical inference of partially linear regression models with heteroscedastic errors

Jinhong You; Gemai Chen; Yong Zhou

doi:10.1016/j.jmva.2007.06.011

Statistical inference of partially linear regression models with heteroscedastic errors

Jinhong You^*
, Gemai Chen
, Yong Zhou

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

34 Scopus citations

Abstract

The authors study a heteroscedastic partially linear regression model and develop an inferential procedure for it. This includes a test of heteroscedasticity, a two-step estimator of the heteroscedastic variance function, semiparametric generalized least-squares estimators of the parametric and nonparametric components of the model, and a bootstrap goodness of fit test to see whether the nonparametric component can be parametrized.

Original language	English
Pages (from-to)	1539-1557
Number of pages	19
Journal	Journal of Multivariate Analysis
Volume	98
Issue number	8
DOIs	https://doi.org/10.1016/j.jmva.2007.06.011
State	Published - Sep 2007
Externally published	Yes

Keywords

Asymptotic normality
Heteroscedasticity
Local polynomial
Model selection
Semiparametric regression model

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10.1016/j.jmva.2007.06.011

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title = "Statistical inference of partially linear regression models with heteroscedastic errors",

abstract = "The authors study a heteroscedastic partially linear regression model and develop an inferential procedure for it. This includes a test of heteroscedasticity, a two-step estimator of the heteroscedastic variance function, semiparametric generalized least-squares estimators of the parametric and nonparametric components of the model, and a bootstrap goodness of fit test to see whether the nonparametric component can be parametrized.",

keywords = "Asymptotic normality, Heteroscedasticity, Local polynomial, Model selection, Semiparametric regression model",

author = "Jinhong You and Gemai Chen and Yong Zhou",

year = "2007",

month = sep,

doi = "10.1016/j.jmva.2007.06.011",

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journal = "Journal of Multivariate Analysis",

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AU - You, Jinhong

AU - Chen, Gemai

AU - Zhou, Yong

PY - 2007/9

Y1 - 2007/9

N2 - The authors study a heteroscedastic partially linear regression model and develop an inferential procedure for it. This includes a test of heteroscedasticity, a two-step estimator of the heteroscedastic variance function, semiparametric generalized least-squares estimators of the parametric and nonparametric components of the model, and a bootstrap goodness of fit test to see whether the nonparametric component can be parametrized.

AB - The authors study a heteroscedastic partially linear regression model and develop an inferential procedure for it. This includes a test of heteroscedasticity, a two-step estimator of the heteroscedastic variance function, semiparametric generalized least-squares estimators of the parametric and nonparametric components of the model, and a bootstrap goodness of fit test to see whether the nonparametric component can be parametrized.

KW - Asymptotic normality

KW - Heteroscedasticity

KW - Local polynomial

KW - Model selection

KW - Semiparametric regression model

UR - https://www.scopus.com/pages/publications/34548160703

U2 - 10.1016/j.jmva.2007.06.011

DO - 10.1016/j.jmva.2007.06.011

M3 - 文章

AN - SCOPUS:34548160703

SN - 0047-259X

VL - 98

SP - 1539

EP - 1557

JO - Journal of Multivariate Analysis

JF - Journal of Multivariate Analysis

IS - 8

ER -

Statistical inference of partially linear regression models with heteroscedastic errors

Abstract

Keywords

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