|
Activity Number:
|
103
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 7, 2006 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Biometrics Section
|
| Abstract - #305456 |
|
Title:
|
Adaptive-LASSO for Cox's Proportional Hazards Model
|
|
Author(s):
|
Wenbin Lu*+ and Hao Zhang
|
|
Companies:
|
North Carolina State University and North Carolina State University
|
|
Address:
|
210E Patterson Hall, Raleigh, NC, 27695,
|
|
Keywords:
|
adaptive LASSO (ALASSO) ; LASSO ; penalized partial likelihood ; proportional hazards model ; variable selection
|
|
Abstract:
|
We investigate the variable selection problem for Cox's proportional hazards model and propose a unified model selection and estimation procedure with desired theoretical properties and computational convenience. The new method is based on a penalized log partial likelihood with the adaptively weighted L_1 penalty on regression coefficients and is named adaptive-LASSO (ALASSO) estimator. Instead of applying the same penalty to all the coefficients as other shrinkage methods, the ALASSO advocates different penalties for different coefficients: Unimportant variables receive larger penalties than important variables. In this way, important variables can be protectively preserved in the model selection process, while unimportant ones are shrunk more toward zero and thus more likely to be dropped from the model. We study the consistency and oracle properties of the proposed estimator.
|
