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