JSM 2012 Home

JSM 2012 Online Program

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: 356
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #306925
Title: Robust Feature Screening and Selectionfor Ultrahigh Dimensional Heteroscedastic Single-Index Models
Author(s): Wei Zhong*+ and Runze Li and Liping Zhu
Companies: Penn State University and Penn State University and Shanghai University of Finance and Economics
Address: Department of Statistics, University Park, PA, 16802,
Keywords:
Abstract:

In this paper, we propose a two-stage feature screening and variable selection procedure to study the estimation of the index parameter in heteroscedastic single-index models with ultrahigh dimensional covariates. In the screening stage, we propose a robust independent ranking and screening (RIRS) procedure to reduce the ultrahigh dimensionality of the covariates to a moderate scale. Aside from its computational simplicity, the RIRS procedure maintains the ranking consistency property and the sure screening property. Therefore, in an asymptotic sense the RIRS procedure guarantees to retain all the truly active predictors. However, some inactive predictors may be selected as well. In the cleaning stage, we propose penalized linear quantile regression to refine the selection of the preceding RIRS procedure, and to simultaneously estimate the direction of the index parameter. We establish the consistency and the oracle property of the resulting penalized estimator, and demonstrate through comprehensive numerical studies that the two-stage estimation procedure is computationally expedient and presents an outstanding finite sample performance.


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.