FSDM 2005

 Home    

Professor Trevor Hastie  

Department of Statistics, Stanford University

Division of Biostatistics, Health, Research and Policy Department, Stanford School of Medicine

Model building and feature selection with genomic data

(Joint work with Hui Zou)

One of the challenges with genomic data is building predictive models using thousands of genes. We not only look for good predictors, but we would also like to select a small instrumental subset of the genes. These problems generalize to a number of similar scenarios, all sharing the characterization that "p>>n". In this talk, we review the lasso procedure, which has severe shortcomings when p>>n -- at most n variables are selected! We then propose the ElasticNet, which overcomes this problem, and has the ability to select groups of variables at a time.
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 For Authors
 Schedule
 Keynote Speakers
 Organization