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