Comparison of Poisson Mixture Models for Count Data Clusterization

Abstract. Five methods for count data clusterization based on Poisson mixture models are described. Two of them are parametric, the others are semi-parametric. The methods emlploy the plug-in Bayes classification rule. Their performance is investigated by making use of computer simulation and compared mainly by the clusterization error rate. We also apply the clusterization procedures to real count data and discuss the results. Key words: count data, clusterization, nonparametric Poisson mixtures, plug-in Bayes classification rule, maximum likelihood estimator, classification error rate.

• Type: Journal
• Year: 2002
• Where: INFORMATICALT
• Authors: Jurgis Susinskas, Marijus Radavicius
• Keywords: Bayes Classification Rule, INFORMATICALT 2002, Plug-in Bayes Classification, Poisson Mixtures