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 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.

• Type: Conference
• Year: 2004
• Where: WSC
• Authors: Zdravko I. Botev, Dirk P. Kroese
• Keywords: Global Likelihood Maximization, Inferior Local Maximum, Likelihood Function, Modeling And Simulation, WSC 2004