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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/46523
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dc.contributor.authorGao, Jitien
dc.contributor.authorWolff, Rodneyen
dc.contributor.authorAnh, Voen
dc.date.issued2001en
dc.identifier.citationJournal of Complexity, 2001; 17(4):754-772en
dc.identifier.urihttp://hdl.handle.net/2440/46523-
dc.description.abstractIn 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.en
dc.description.statementofresponsibilityJiti Gao, Rodney Wolff and Vo Anhen
dc.description.urihttp://www.elsevier.com/wps/find/journaldescription.cws_home/622865/description#descriptionen
dc.publisherAcademic Press / Elsevieren
dc.subjectdimensional reduction; linear regression; model selection; nonlinear regression; nonlinear time series; nonparametric regression; semiparametric regressionen
dc.subjectdimensional reduction; linear regression; model selection; nonlinear regression; nonlinear time series; nonparametric regression; semiparametric regressionen
dc.titleSemiparametric approximation methods in multivariate model selectionen
dc.typeJournal articleen
dc.contributor.schoolSchool of Economicsen
dc.identifier.doi10.1006/jcom.2001.0591en
Appears in Collections:Economics publications

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