Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/36284
Title: Group selection in the cox model with a diverging number of Covariates
Authors: Huang, J
Liu, L
Liu, YY
Zhao, XQ 
Keywords: Bi-level selection
Coordinate descent algorithm
Cox regression
Group bridge penalty
Survival data
Variable selection consistency
Issue Date: 2014
Publisher: Academia Sinica, Institute of Statistical Science
Source: Statistica sinica, 2014, v. 24, no. 4, p. 1787-1810 How to cite?
Journal: Statistica sinica 
Abstract: In this article, we propose a variable selection approach in the Cox model when there is a group structure in a diverging number of covariates. Most of the existing variable selection methods are designed for either individual variable selection or group selection, but not for both. The proposed methods are capable of simultaneous group selection and individual variable selection within selected groups. Computational algorithms are developed for the proposed bi-level selection methods, and the properties of the proposed selection methods are established. The proposed group bridge penalized methods are able to correctly select the important groups and variables simultaneously with high probability in sparse models. Simulation studies indicate that the proposed methods work well and two examples are provided to illustrate the applications of the proposed methods to scientific problems.
URI: http://hdl.handle.net/10397/36284
ISSN: 1017-0405
DOI: 10.5705/ss.2013.061
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