Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/46523
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Type: Journal article
Title: Semiparametric approximation methods in multivariate model selection
Author: Gao, Jiti
Wolff, Rodney
Anh, Vo
Citation: Journal of Complexity, 2001; 17(4):754-772
Publisher: Academic Press / Elsevier
Issue Date: 2001
School/Discipline: School of Economics
Statement of
Responsibility: 
Jiti Gao, Rodney Wolff and Vo Anh
Abstract: In this paper we propose a cross-validation selection criterion to determine asymptotically the correct model among the family of all possible partially linear models when the underlying model is a partially linear model. We establish the asymptotic consistency of the criterion. In addition, the criterion is illustrated using two real sets of data.
Keywords: dimensional reduction; linear regression; model selection; nonlinear regression; nonlinear time series; nonparametric regression; semiparametric regression
dimensional reduction; linear regression; model selection; nonlinear regression; nonlinear time series; nonparametric regression; semiparametric regression
DOI: 10.1006/jcom.2001.0591
Description (link): http://www.elsevier.com/wps/find/journaldescription.cws_home/622865/description#description
Appears in Collections:Economics publications

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