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arXiv · 0903.0499

Statistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates

Yong Zhou·Hua Liang
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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. 2009-03-03. Statistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates. https://doi.org/10.1214/07-aos561

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