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: 466
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #305427
Title: The Sparse-MLE for Variable Screening in Ultra-High-Dimensional Feature Space
Author(s): Chen Xu*+ and Jiahua Chen
Companies: University of British Columbia and SSC
Address: 333-6356 Agricultural Road, Vancouver, BC, V6T 1Z2, Canada
Keywords: Variable screening ; Screening consistency ; Sparse MLE ; High dimensionality ; Penalized likelihood ; Hard thresholding
Abstract:

Variable selection and feature extraction are fundamental for knowledge discovery and statistical modeling with high-dimensionality. For the computational feasibility, reducing the dimension of data is a necessary step before the formal analysis. Fan and Lv (2008) suggested a variable screening strategy based on the marginal correlations between the covariates and the response. In the same spirit, we propose a new screening approach via the sparsity-restricted maximum likelihood estimator (sparse-MLE) to account for more joint effects among the covariates. The new approach efficiently screens out most irrelevant variables from the model while retains those important ones with high probability. The low-dimensional model produced by sparse-MLE then serves as a good starting point for the further selection. We establish the screening consistency of the sparse-MLE and further develop an efficient algorithm for its implementation. The excellent performances of proposed method are supported by extensive numerical studies.


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.