"Everything should be made as simple as possible, but not simpler." - Albert Einstein

Seminar Announcement

Penalized Empirical Likelihood and Growing Dimensional General Estimating Equations

Cheng Yong Tang, National University of Singapore

Monday, January 23, 2012

4:00 p.m., room 237, Weber Bldg

ABSTRACT

When a parametric likelihood function is not specified for a model, estimating equations may provide an instrument for statistical inference. Qin and Lawless (1994) illustrate that empirical likelihood makes optimal use of these equations in inferences for fixed low dimensional unknown parameters. In this paper, we study empirical likelihood for general estimating equations with growing high dimensionality and propose a penalized empirical likelihood approach for parameter estimation and variable selection. We quantify the asymptotic properties of empirical likelihood and its penalized version, and show that penalized empirical likelihood has the oracle property. The performance of the proposed method is illustrated via simulated applications and a data analysis. This is a joint work with Chenlei Leng.