The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Online Program Home
Abstract Details
Activity Number:
|
75
|
Type:
|
Contributed
|
Date/Time:
|
Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
Abstract - #306795 |
Title:
|
Groupwise Sufficient Dimension Reduction via Envelope Method
|
Author(s):
|
Zifang Guo*+ and Lexin Li and Wenbin Lu
|
Companies:
|
North Carolina State University and North Carolina State University and North Carolina State University
|
Address:
|
, , ,
|
Keywords:
|
Central subspace ;
Direct sum envelope ;
Groupwise dimension reduction
|
Abstract:
|
In many dimension reduction problems, the predictors come from different groups, and it is often desirable to incorporate such prior group information into dimension reduction procedures to obtain more informative and more interpretable estimates. In this article, we propose a groupwise dimension reduction method which aims to recover full regression information in the conditional distribution of response given the predictors, while preserving the group structure of the predictors. The proposed method is based on imposing a group structure onto classical central subspace estimators via a direct sum envelope. Simulation studies and real data analysis show that the proposed method achieves competent nite sample performance, in terms of both estimation accuracy and interpretability of the resulting estimator.
|
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 2012 program
|
2012 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.