Semiparametric Approximation Methods in Multivariate Model Selection
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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|Item Type:||Journal Article|
|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|
|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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