Semiparametric Approximation Methods in Multivariate Model Selection

Gao, Jiti, Wolff, Rodney C., & Anh, Vo V. (2001) Semiparametric Approximation Methods in Multivariate Model Selection. Journal of Complexity, 17(4), pp. 754-772.

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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.

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7 citations in Scopus
2 citations in Web of Science®
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ID Code: 9767
Item Type: Journal Article
Refereed: Yes
Additional Information: For more information, please refer to the journal’s website (see hypertext link) or contact the author.
Keywords: dimensional reduction, linear regression, model selection, nonlinear regression, nonlinear time series, nonparametric regression, semiparametric regression
DOI: 10.1006/jcom.2001.0591
ISSN: 0885-064X
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2001 Elsevier
Deposited On: 26 Sep 2007 00:00
Last Modified: 15 Jan 2009 07:47

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