This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 658
Type: Topic Contributed
Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #306798
Title: Penalized Empirical Likelihood for High-Dimensional General Estimating Equations
Author(s): Chenlei Leng*+ and Chengyong Tang
Companies: National University of Singapore and National University of Singapore
Address: , , International, 117546, Singapore
Keywords: Empirical likelihood ; General estimating equations ; Smoothly clipped absolute deviation ; Variable selection
Abstract:

When a parametric likelihood function is not specified for a model, the estimating equations (EE) method provides an instrument for statistical inference. In this paper, we study the empirical likelihood (EL) method for general EE with growing dimensionality and propose an EE based penalized empirical likelihood (PEL) approach for parameter estimation and variable selection. Theoretically, we quantify the asymptotic properties of the EL and PEL methods. We show that the PEL method has the Oracle property. In addition, the efficiency of the estimated nonzero coefficients is optimal. The performance of the proposed PEL method is illustrated via extensive simulattion studies and a data analysis.


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