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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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