JSM 2011 Online Program

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Abstract Details

Activity Number: 304
Type: Contributed
Date/Time: Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #302364
Title: Application of Evolutionary Algorithms in Estimation of Empirical Likelihoods
Author(s): Ashley Askew*+
Companies: University of Georgia
Address: 101 Cedar Street, Athens, GA, 30602,
Keywords: Evolutionary algorithm ; Empirical Likelihood

An evolutionary algorithm (EA) is a flexible global optimization routine, as well as an alternative to the conventional numerical methods based on derivatives. Empirical likelihoods (ELs) are a non-parametric formulation that utilizes the data to construct a likelihood. The estimation of ELs can pose challenges, especially when constraints must be incorporated in the likelihood. In this work, we compare the performance of a penalized EA against a numerical procedure based on Newton-Raphson in estimating various examples of ELs.

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