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Article: Rank-based variable selection with censored data

TitleRank-based variable selection with censored data
Authors
KeywordsAccelerated failure time model
Adaptive Lasso
BIC
Gehan-type loss function
Lasso
Variable selection
Issue Date2010
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0960-3174
Citation
Statistics and Computing, 2010, v. 20, p. 165-176 How to Cite?
AbstractA rank-based variable selection procedure is developed for the semiparametric accelerated failure time model with censored observations where the penalized likelihood (partial likelihood) method is not directly applicable. The new method penalizes the rank-based Gehan-type loss function with the penalty. To correctly choose the tuning parameters, a novel likelihood-based -type criterion is proposed. Desirable properties of the estimator such as the oracle properties are established through the local quadratic expansion of the Gehan loss function. In particular, our method can be easily implemented by the standard linear programming packages and hence numerically convenient. Extensions to marginal models for multivariate failure time are also considered. The performance of the new procedure is assessed through extensive simulation studies and illustrated with two real examples.
Persistent Identifierhttp://hdl.handle.net/10722/221684
ISSN
2015 Impact Factor: 1.786
2015 SCImago Journal Rankings: 1.993
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXu, J-
dc.contributor.authorLeng, C-
dc.contributor.authorYing, Z-
dc.date.accessioned2015-12-04T15:29:06Z-
dc.date.available2015-12-04T15:29:06Z-
dc.date.issued2010-
dc.identifier.citationStatistics and Computing, 2010, v. 20, p. 165-176-
dc.identifier.issn0960-3174-
dc.identifier.urihttp://hdl.handle.net/10722/221684-
dc.description.abstractA rank-based variable selection procedure is developed for the semiparametric accelerated failure time model with censored observations where the penalized likelihood (partial likelihood) method is not directly applicable. The new method penalizes the rank-based Gehan-type loss function with the penalty. To correctly choose the tuning parameters, a novel likelihood-based -type criterion is proposed. Desirable properties of the estimator such as the oracle properties are established through the local quadratic expansion of the Gehan loss function. In particular, our method can be easily implemented by the standard linear programming packages and hence numerically convenient. Extensions to marginal models for multivariate failure time are also considered. The performance of the new procedure is assessed through extensive simulation studies and illustrated with two real examples.-
dc.languageeng-
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0960-3174-
dc.relation.ispartofStatistics and Computing-
dc.subjectAccelerated failure time model-
dc.subjectAdaptive Lasso-
dc.subjectBIC-
dc.subjectGehan-type loss function-
dc.subjectLasso-
dc.subjectVariable selection-
dc.titleRank-based variable selection with censored data-
dc.typeArticle-
dc.identifier.emailXu, J: xujf@hku.hk-
dc.identifier.authorityXu, J=rp02086-
dc.identifier.doi10.1007/s11222-009-9126-y-
dc.identifier.scopuseid_2-s2.0-77953326454-
dc.identifier.volume20-
dc.identifier.spage165-
dc.identifier.epage176-
dc.identifier.isiWOS:000276075700005-

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