Comparison of Poisson Mixture Models for Count Data Clusterization
Volume 13, Issue 2 (2002), pp. 209–226
Pub. online: 1 January 2002
Type: Research Article
Received
1 March 2002
1 March 2002
Published
1 January 2002
1 January 2002
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.