Statistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates
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.
Cite
@article{arxiv.0903.0499,
title = {Statistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates},
author = {Yong Zhou and Hua Liang},
journal= {arXiv preprint arXiv:0903.0499},
year = {2009}
}
Comments
Published in at http://dx.doi.org/10.1214/07-AOS561 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)