Abstract Details
Activity Number:
|
350
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Biometrics Section
|
Abstract - #310190 |
Title:
|
Joint Structure Selection and Estimation in Time-Varying Coefficient Cox Model
|
Author(s):
|
Wei Xiao*+ and Wenbin Lu
|
Companies:
|
North Carolina State University and Department of Statistics, North Carolina State University
|
Keywords:
|
Time-varying coefficient Cox model ;
Model selection ;
Group non-negative garrote ;
Local polynomial smoothing ;
Partial likelihood ;
Kernel
|
Abstract:
|
Time-varying coefficient Cox model has been widely studied and popularly used in survival data analysis due to its flexibility for modeling covariate effects. It is of great practical interest to accurately identify the structure of covariate effects in a time-varying coefficient Cox model, i.e. covariates with null effect, constant effect and truly time-varying effect, and estimate the corresponding regression coefficients. Combining the ideas of local polynomial smoothing and group non-negative garrote, we develop a new penalization approach to achieve such goals. Our method is able to identify the underlying true model structure with probability tending to one and simultaneously estimate the time-varying coefficients consistently. The asymptotic properties of the resulting estimators are established. We demonstrate the performance of our method using simulations and an application to the primary biliary cirrhosis data.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.