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

Shan Luo; Jinfeng Xu; Zehua Chen

doi:10.1007/s10463-014-0448-y

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

Shan Luo^*, Jinfeng Xu, Zehua Chen

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

22 Citations (Scopus)

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.

Original language	English
Pages (from-to)	287-311
Journal	Annals of the Institute of Statistical Mathematics
Volume	67
Issue number	2
DOIs	https://doi.org/10.1007/s10463-014-0448-y
Publication status	Published - 1 Apr 2015
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

Cox model
Extended Bayesian information criterion
Selection consistency
Variable selection

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title = "Extended Bayesian information criterion in the Cox model with a high-dimensional feature space",

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.",

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T1 - Extended Bayesian information criterion in the Cox model with a high-dimensional feature space

AU - Luo, Shan

AU - Xu, Jinfeng

AU - Chen, Zehua

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2015/4/1

Y1 - 2015/4/1

N2 - 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.

AB - 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.

KW - Cox model

KW - Extended Bayesian information criterion

KW - Selection consistency

KW - Variable selection

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DO - 10.1007/s10463-014-0448-y

M3 - RGC 21 - Publication in refereed journal

SN - 0020-3157

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EP - 311

JO - Annals of the Institute of Statistical Mathematics

JF - Annals of the Institute of Statistical Mathematics

IS - 2

ER -

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

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Research Keywords

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