Article ID Journal Published Year Pages File Type
1147880 Journal of Statistical Planning and Inference 2013 15 Pages PDF
Abstract

This paper studies the partially time-varying coefficient models where some covariates are measured with additive errors. In order to overcome the bias of the usual profile least squares estimation when measurement errors are ignored, we propose a modified profile least squares estimator of the regression parameter and construct estimators of the nonlinear coefficient function and error variance. The proposed three estimators are proved to be asymptotically normal under mild conditions. In addition, we introduce the profile likelihood ratio test and then demonstrate that it follows an asymptotically χ2χ2 distribution under the null hypothesis. Finite sample behavior of the estimators is investigated via simulations too.

Related Topics
Physical Sciences and Engineering Mathematics Applied Mathematics
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