Please use this identifier to cite or link to this item: https://doi.org/10.5705/ss.2010.105
DC FieldValue
dc.titleAdaptive semi-varying coefficient model selection
dc.contributor.authorHu, T.
dc.contributor.authorXia, Y.
dc.date.accessioned2014-10-28T05:09:53Z
dc.date.available2014-10-28T05:09:53Z
dc.date.issued2012-04
dc.identifier.citationHu, T., Xia, Y. (2012-04). Adaptive semi-varying coefficient model selection. Statistica Sinica 22 (2) : 575-599. ScholarBank@NUS Repository. https://doi.org/10.5705/ss.2010.105
dc.identifier.issn10170405
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/104986
dc.description.abstractIdentification of constant coefficients in a semi-varying coefficient model is an important issue (Zhang et al (2002)). We propose a novel method for this by combining local polynomial smoothing (Fan and Zhang (1999)) with shrinkage estimation (Tibshirani (1996)). Unlike the stepwise procedure (Xia, Zhang, and Tong (2004)), our method can identify the constant coefficients and estimate the model simultaneously. By imposing the adaptive LASSO penalty and starting with the Nadaraya-Watson estimator, the method can identify the constant coefficients and varying coefficients consistently, and estimate the model with oracle efficiency (Fan and Li (2001)).
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.5705/ss.2010.105
dc.sourceScopus
dc.subjectBIC
dc.subjectKernel smoothing
dc.subjectLASSO
dc.subjectModel selection
dc.subjectOracle property
dc.subjectSCAD
dc.subjectSemi-varying coefficient model
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.5705/ss.2010.105
dc.description.sourcetitleStatistica Sinica
dc.description.volume22
dc.description.issue2
dc.description.page575-599
dc.identifier.isiut000303963100007
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