Semiparametric penalty function method in partially linear model selection

dc.contributor.authorDong, C.
dc.contributor.authorGao, J.
dc.contributor.authorTong, H.
dc.date.issued2007
dc.description.abstractModel selection in nonparametric and semiparametric regression is of both theoretical and practical interest. Gao and Tong (2004) proposed a semiparametric leave-more-out cross-validation selection procedure for the choice of both the parametric and nonparametric regressors in a nonlinear time series regression model. As recognized by the authors, the implementation of the proposed procedure requires the availability of relatively large sample sizes. In order to address the model selection problem with small or medium sample sizes, we propose a model selection procedure for practical use. By extending the so-called penalty function method proposed in Zheng and Loh (1995, 1997) through the incorporation of features of the leave-one-out cross-validation approach, we develop a semiparametric, consistent selection procedure suitable for the choice of optimum subsets in a partially linear model. The newly proposed method is implemented using the full set of data, and simulations show that it works well for both small and medium sample sizes.
dc.description.statementofresponsibilityChaohua Dong, Jiti Gao and Howell Tong
dc.identifier.citationStatistica Sinica, 2007; 17(1):99-114
dc.identifier.issn1017-0405
dc.identifier.urihttp://hdl.handle.net/2440/46465
dc.language.isoen
dc.publisherStatistica Sinica
dc.source.urihttp://www3.stat.sinica.edu.tw/statistica/J17N1/J17N15/J17N15.html
dc.subjectLinear model, model selection, nonparametric method, partially linear model, semiparametric method.
dc.titleSemiparametric penalty function method in partially linear model selection
dc.typeJournal article
pubs.publication-statusPublished

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