File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Extended Bayesian information criterion in the Cox model with a high-dimensional feature space

TitleExtended Bayesian information criterion in the Cox model with a high-dimensional feature space
Authors
KeywordsVariable selection
Cox model
Extended Bayesian information criterion
Selection consistency
Issue Date2015
Citation
Annals of the Institute of Statistical Mathematics, 2015, v. 67, p. 287-311 How to Cite?
AbstractVariable 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 Identifierhttp://hdl.handle.net/10722/221693
ISSN
2015 Impact Factor: 0.768
2015 SCImago Journal Rankings: 0.931
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLuo, S-
dc.contributor.authorXu, J-
dc.contributor.authorChen, Z-
dc.date.accessioned2015-12-04T15:29:09Z-
dc.date.available2015-12-04T15:29:09Z-
dc.date.issued2015-
dc.identifier.citationAnnals of the Institute of Statistical Mathematics, 2015, v. 67, p. 287-311-
dc.identifier.issn00203157-
dc.identifier.urihttp://hdl.handle.net/10722/221693-
dc.description.abstractVariable 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.languageeng-
dc.relation.ispartofAnnals of the Institute of Statistical Mathematics-
dc.subjectVariable selection-
dc.subjectCox model-
dc.subjectExtended Bayesian information criterion-
dc.subjectSelection consistency-
dc.titleExtended Bayesian information criterion in the Cox model with a high-dimensional feature space-
dc.typeArticle-
dc.identifier.emailXu, J: xujf@hku.hk-
dc.identifier.authorityXu, J=rp02086-
dc.identifier.doi10.1007/s10463-014-0448-y-
dc.identifier.scopuseid_2-s2.0-84923702695-
dc.identifier.hkuros260474-
dc.identifier.volume67-
dc.identifier.spage287-
dc.identifier.epage311-
dc.identifier.isiWOS:000350235400004-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats