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- Publisher Website: 10.1007/s10463-014-0448-y
- Scopus: eid_2-s2.0-84923702695
- WOS: WOS:000350235400004
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Article: Extended Bayesian information criterion in the Cox model with a high-dimensional feature space
Title | Extended Bayesian information criterion in the Cox model with a high-dimensional feature space |
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Authors | |
Keywords | Variable selection Cox model Extended Bayesian information criterion Selection consistency |
Issue Date | 2015 |
Citation | Annals of the Institute of Statistical Mathematics, 2015, v. 67, p. 287-311 How to Cite? |
Abstract | Variable selection in the Cox proportional hazards model (the Cox model) has manifested its importance in many microarray genetic studies. However, theoretical results on the procedures of variable selection in the Cox model with a high-dimensional feature space are rare because of its complicated data structure. In this paper, we consider the extended Bayesian information criterion (EBIC) for variable selection in the Cox model and establish its selection consistency in the situation of high-dimensional feature space. The EBIC is adopted to select the best model from a model sequence generated from the SIS-ALasso procedure. Simulation studies and real data analysis are carried out to demonstrate the merits of the EBIC. |
Persistent Identifier | http://hdl.handle.net/10722/221693 |
ISSN | 2023 Impact Factor: 0.8 2023 SCImago Journal Rankings: 0.791 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Luo, S | - |
dc.contributor.author | Xu, J | - |
dc.contributor.author | Chen, Z | - |
dc.date.accessioned | 2015-12-04T15:29:09Z | - |
dc.date.available | 2015-12-04T15:29:09Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Annals of the Institute of Statistical Mathematics, 2015, v. 67, p. 287-311 | - |
dc.identifier.issn | 0020-3157 | - |
dc.identifier.uri | http://hdl.handle.net/10722/221693 | - |
dc.description.abstract | Variable selection in the Cox proportional hazards model (the Cox model) has manifested its importance in many microarray genetic studies. However, theoretical results on the procedures of variable selection in the Cox model with a high-dimensional feature space are rare because of its complicated data structure. In this paper, we consider the extended Bayesian information criterion (EBIC) for variable selection in the Cox model and establish its selection consistency in the situation of high-dimensional feature space. The EBIC is adopted to select the best model from a model sequence generated from the SIS-ALasso procedure. Simulation studies and real data analysis are carried out to demonstrate the merits of the EBIC. | - |
dc.language | eng | - |
dc.relation.ispartof | Annals of the Institute of Statistical Mathematics | - |
dc.subject | Variable selection | - |
dc.subject | Cox model | - |
dc.subject | Extended Bayesian information criterion | - |
dc.subject | Selection consistency | - |
dc.title | Extended Bayesian information criterion in the Cox model with a high-dimensional feature space | - |
dc.type | Article | - |
dc.identifier.email | Xu, J: xujf@hku.hk | - |
dc.identifier.authority | Xu, J=rp02086 | - |
dc.identifier.doi | 10.1007/s10463-014-0448-y | - |
dc.identifier.scopus | eid_2-s2.0-84923702695 | - |
dc.identifier.hkuros | 260474 | - |
dc.identifier.volume | 67 | - |
dc.identifier.spage | 287 | - |
dc.identifier.epage | 311 | - |
dc.identifier.isi | WOS:000350235400004 | - |
dc.identifier.issnl | 0020-3157 | - |