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- Publisher Website: 10.1007/s11222-009-9126-y
- Scopus: eid_2-s2.0-77953326454
- WOS: WOS:000276075700005
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Article: Rank-based variable selection with censored data
Title | Rank-based variable selection with censored data |
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Authors | |
Keywords | Accelerated failure time model Adaptive Lasso BIC Gehan-type loss function Lasso Variable selection |
Issue Date | 2010 |
Publisher | Springer 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? |
Abstract | A 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 Identifier | http://hdl.handle.net/10722/221684 |
ISSN | 2023 Impact Factor: 1.6 2023 SCImago Journal Rankings: 0.923 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Xu, J | - |
dc.contributor.author | Leng, C | - |
dc.contributor.author | Ying, Z | - |
dc.date.accessioned | 2015-12-04T15:29:06Z | - |
dc.date.available | 2015-12-04T15:29:06Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | Statistics and Computing, 2010, v. 20, p. 165-176 | - |
dc.identifier.issn | 0960-3174 | - |
dc.identifier.uri | http://hdl.handle.net/10722/221684 | - |
dc.description.abstract | A 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.language | eng | - |
dc.publisher | Springer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0960-3174 | - |
dc.relation.ispartof | Statistics and Computing | - |
dc.subject | Accelerated failure time model | - |
dc.subject | Adaptive Lasso | - |
dc.subject | BIC | - |
dc.subject | Gehan-type loss function | - |
dc.subject | Lasso | - |
dc.subject | Variable selection | - |
dc.title | Rank-based variable selection with censored data | - |
dc.type | Article | - |
dc.identifier.email | Xu, J: xujf@hku.hk | - |
dc.identifier.authority | Xu, J=rp02086 | - |
dc.identifier.doi | 10.1007/s11222-009-9126-y | - |
dc.identifier.scopus | eid_2-s2.0-77953326454 | - |
dc.identifier.volume | 20 | - |
dc.identifier.spage | 165 | - |
dc.identifier.epage | 176 | - |
dc.identifier.isi | WOS:000276075700005 | - |
dc.identifier.issnl | 0960-3174 | - |