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

Details High-dimensional variable selection for Cox's proportional hazards model High-dimensional variable selection for Cox's proportional hazards model

2010-02-17

arxiv.org/abs/1002.3315v2

Statistics

Machine Learning

17 pages, 5 figures

Scientific paper

Variable selection in high dimensional space has challenged many contemporary statistical problems from many frontiers of scientific disciplines. Recent technology advance has 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 to the 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 Fan and Lv (2008) 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 independent screening scheme.

Affiliated with

Also associated with

No associations

Rating

High-dimensional variable selection for Cox's proportional hazards model does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with High-dimensional variable selection for Cox's proportional hazards model, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and High-dimensional variable selection for Cox's proportional hazards model will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-52223