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February 2009 Statistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates
Yong Zhou, Hua Liang
Ann. Statist. 37(1): 427-458 (February 2009). DOI: 10.1214/07-AOS561

Abstract

We study semiparametric varying-coefficient partially linear models when some linear covariates are not observed, but ancillary variables are available. Semiparametric profile least-square based estimation procedures are developed for parametric and nonparametric components after we calibrate the error-prone covariates. Asymptotic properties of the proposed estimators are established. We also propose the profile least-square based ratio test and Wald test to identify significant parametric and nonparametric components. To improve accuracy of the proposed tests for small or moderate sample sizes, a wild bootstrap version is also proposed to calculate the critical values. Intensive simulation experiments are conducted to illustrate the proposed approaches.

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Yong Zhou. Hua Liang. "Statistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates." Ann. Statist. 37 (1) 427 - 458, February 2009. https://doi.org/10.1214/07-AOS561

Information

Published: February 2009
First available in Project Euclid: 16 January 2009

zbMATH: 1156.62036
MathSciNet: MR2488358
Digital Object Identifier: 10.1214/07-AOS561

Subjects:
Primary: 62G08, 62G10
Secondary: 62G20, 62H15

Rights: Copyright © 2009 Institute of Mathematical Statistics

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Vol.37 • No. 1 • February 2009
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