Penalized Variable Selection Procedure for Cox Models with Semiparametric Relative Risk

dc.contributor.authorDu, Pangen
dc.contributor.authorMa, Shuanggeen
dc.contributor.authorLiang, Huaen
dc.contributor.departmentStatisticsen
dc.date.accessioned2017-01-23T22:43:19Zen
dc.date.available2017-01-23T22:43:19Zen
dc.date.issued2010-08-01en
dc.description.abstractWe study the Cox models with semiparametric relative risk, which can be partially linear with one nonparametric component, or multiple additive or nonadditive nonparametric components. A penalized partial likelihood procedure is proposed to simultaneously estimate the parameters and select variables for both the parametric and the nonparametric parts. Two penalties are applied sequentially. The first penalty, governing the smoothness of the multivariate nonlinear covariate effect function, provides a smoothing spline ANOVA framework that is exploited to derive an empirical model selection tool for the nonparametric part. The second penalty, either the smoothly-clipped-absolute-deviation (SCAD) penalty or the adaptive LASSO penalty, achieves variable selection in the parametric part. We show that the resulting estimator of the parametric part possesses the oracle property, and that the estimator of the nonparametric part achieves the optimal rate of convergence. The proposed procedures are shown to work well in simulation experiments, and then applied to a real data example on sexually transmitted diseases.en
dc.description.versionPublished versionen
dc.format.extent2092 - 2117 (26) page(s)en
dc.identifier.doihttps://doi.org/10.1214/09-AOS780en
dc.identifier.issn0090-5364en
dc.identifier.issue4en
dc.identifier.urihttp://hdl.handle.net/10919/74411en
dc.identifier.volume38en
dc.language.isoenen
dc.publisherInst Mathematical Statisticsen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000280359400006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectStatistics & Probabilityen
dc.subjectMathematicsen
dc.subjectSTATISTICS & PROBABILITYen
dc.subjectBackfittingen
dc.subjectpartially linear modelsen
dc.subjectpenalized variable selectionen
dc.subjectproportional hazardsen
dc.subjectpenalized partial likelihooden
dc.subjectsmoothing spline ANOVAen
dc.subjectPROPORTIONAL HAZARDS REGRESSIONen
dc.subjectPARTIAL LIKELIHOODen
dc.subjectORACLE PROPERTIESen
dc.subjectLASSOen
dc.titlePenalized Variable Selection Procedure for Cox Models with Semiparametric Relative Risken
dc.title.serialAnnals of Statisticsen
dc.typeArticle - Refereeden
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Scienceen
pubs.organisational-group/Virginia Tech/Science/COS T&R Facultyen
pubs.organisational-group/Virginia Tech/Science/Statisticsen

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
1010.3855v1.pdf
Size:
363.73 KB
Format:
Adobe Portable Document Format
Description:
Publisher's Version
License bundle
Now showing 1 - 1 of 1
Name:
VTUL_Distribution_License_2016_05_09.pdf
Size:
18.09 KB
Format:
Adobe Portable Document Format
Description: