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

Global likelihood maximization is an important aspect of many statistical analyses. Often the likelihood function is highly multiextremal. 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.