Global likelihood optimization via the cross-entropy method with an application to mixture models

Authors:
Zdravko Botev;Dirk P. Kroese
Affiliations:
The University of Queensland, Brisbane, Australia;The University of Queensland, Brisbane, Australia
Venue:
WSC '04 Proceedings of the 36th conference on Winter simulation
Year:
2004

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Abstract

Global likelihood maximization is an important aspect of many statistical analyses. Often the likelihood function is highly multi-extremal. This presents a significant challenge to standard search procedures, which often settle too quickly into an inferior local maximum. We present a new approach based on the cross-entropy (CE) method, and illustrate its use for the analysis of mixture models.