|
Activity Number:
|
143
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 4, 2008 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
ENAR
|
| Abstract - #300969 |
|
Title:
|
Tuning Parameter Selections for Penalized Likelihood Functions
|
|
Author(s):
|
Yiyun Zhang*+ and Runze Li and Chih-Ling Tsai
|
|
Companies:
|
The Pennsylvania State University and The Pennsylvania State University and University of California, Davis
|
|
Address:
|
331A Thomas Building, University Park, PA, 16802,
|
|
Keywords:
|
AIC ; BIC ; GIC ; LASSO ; Nonconcave penalized likelihood ; SCAD
|
|
Abstract:
|
We apply the nonconcave penalized likelihood approach to obtain variable selections as well as shrinkage estimators. This approach relies heavily on the choice of regularization parameter, which controls the model complexity. We propose employing the generalized information criterion, encompassing the commonly used AIC and BIC, for selecting the regularization parameter. Our proposal makes a connection between the classical variable selection criteria and the regularization parameter selections for the nonconcave penalized likelihood. We show that the BIC-type selectors enable identification of the true model consistently, and the resulting estimator possesses the oracle property in the terminology of Fan and Li (2001). In contrast, the AIC-type selectors are asymptotic loss efficient under some mild conditions. Our simulation results confirm these theoretical findings.
|
- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
Back to the full JSM 2008 program |
