Statistical inference for partially linear regression models with measurement errors
In this paper, the authors investigate three aspects of statistical inference for the partially linear regression models where some covariates are measured with errors. Firstly, a bandwidth selection procedure is proposed, which is a combination of the differencebased technique and GCV method. Seco... Full description
Journal Title:  Chinese Annals of Mathematics Series B, 2008, Vol.29(2), pp.207222 
Main Author:  You, Jinhong 
Other Authors:  Xu, Qinfeng , Zhou, Bin 
Format:  Electronic Article 
Language: 
English 
Subjects:  
ID:  ISSN: 02529599 ; EISSN: 18606261 ; DOI: 10.1007/s1140100602108 
Link:  http://dx.doi.org/10.1007/s1140100602108 
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recordid:  springer_jour10.1007/s1140100602108 
title:  Statistical inference for partially linear regression models with measurement errors 
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ispartof:  Chinese Annals of Mathematics, Series B, 2008, Vol.29(2), pp.207222 
description:  In this paper, the authors investigate three aspects of statistical inference for the partially linear regression models where some covariates are measured with errors. Firstly, a bandwidth selection procedure is proposed, which is a combination of the differencebased technique and GCV method. Secondly, a goodnessoffit test procedure is proposed, which is an extension of the generalized likelihood technique. Thirdly, a variable selection procedure for the parametric part is provided based on the nonconcave penalization and corrected profile least squares. Same as “Variable selection via nonconcave penalized likelihood and its oracle properties” (J. Amer. Statist. Assoc., 96 , 2001, 1348–1360), it is shown that the resulting estimator has an oracle property with a proper choice of regularization parameters and penalty function. Simulation studies are conducted to illustrate the finite sample performances of the proposed procedures. 
language:  eng 
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identifier:  ISSN: 02529599 ; EISSN: 18606261 ; DOI: 10.1007/s1140100602108 
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