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High-dimensional variable selection for Cox’s proportional hazards model

Jianqing Fan, Yang Feng, and Yichao Wu

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Abstract

Variable selection in high dimensional space has challenged many contemporary statistical problems from many frontiers of scientific disciplines. Recent technological advances have made it possible to collect a huge amount of covariate information such as microarray, proteomic and SNP data via bioimaging technology while observing survival information on patients in clinical studies. Thus, the same challenge applies in survival analysis in order to understand the association between genomics information and clinical information about the survival time. In this work, we extend the sure screening procedure [6] to Cox’s proportional hazards model with an iterative version available. Numerical simulation studies have shown encouraging performance of the proposed method in comparison with other techniques such as LASSO. This demonstrates the utility and versatility of the iterative sure independence screening scheme.

Chapter information

Source
James O. Berger, T. Tony Cai and Iain M. Johnstone, eds., Borrowing Strength: Theory Powering Applications – A Festschrift for Lawrence D. Brown (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2010), 70-86

Dates
First available in Project Euclid: 26 October 2010

Permanent link to this document
https://projecteuclid.org/euclid.imsc/1288099013

Digital Object Identifier
doi:10.1214/10-IMSCOLL606

Subjects
Primary: 62N02: Estimation
Secondary: 62J99: None of the above, but in this section

Keywords
Cox’s proportional hazards model variable selection

Rights
Copyright © 2010, Institute of Mathematical Statistics

Citation

Fan, Jianqing; Feng, Yang; Wu, Yichao. High-dimensional variable selection for Cox’s proportional hazards model. Borrowing Strength: Theory Powering Applications – A Festschrift for Lawrence D. Brown, 70--86, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2010. doi:10.1214/10-IMSCOLL606. https://projecteuclid.org/euclid.imsc/1288099013


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