High-dimensional variable selection for Cox’s proportional hazards model
Institute of Mathematical Statistics collectionsPublished 1 January 2010
Jianqing Fan, Yang Feng, Yichao Wu
Citations135
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TL;DR
This work extends the sure screening procedure to Cox's proportional hazards model with an iterative version available and demonstrates the utility and versatility of the iterative sure independence screening scheme.
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.
Keywords
MathematicsBiochemistry, Genetics and Molecular Biology
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