JSM 2011 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.

Abstract Details

Activity Number: 83
Type: Contributed
Date/Time: Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
Sponsor: IMS
Abstract - #300888
Title: Ultra High-Dimensional Variable Selection Consistency with Endogenous Covariates
Author(s): Yuan Liao*+ and Jianqing Fan
Companies: Princeton University and Princeton University
Address: Dept of ORFE, Princeton, NJ, 08544,
Keywords: penalized GMM ; penalized empirical likelihood ; penalty function ; sparsity recovery ; oracle property ; conditional moment restriction

High dimensional models have gained considerable importance in several areas of applications. We consider ultra high dimensional variable selection problem in which the number of regressors grows exponentially fast with the sample size. Sparse modeling has been widely used to deal with high dimensionality, which assumes that many components of the parameter vector are exactly zero. The variable selection is carried out by penalized optimization. We give sufficient and necessary conditions for a general penalized optimization to achieve the consistency for both variable selection and estimation. We then study a conditional moment restricted model, and verify that under mild assumptions, both penalized GMM (PGMM) and penalized empirical likelihood (PEL) satisfy the proposed sufficient conditions. An interesting finding is, when there exists an endogenous variable whose true regression coefficient is zero, the penalized OLS does not satisfy the necessary condition of variable selection regardless of the penalty function used.

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 2011 program

2011 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.