Volume 26, Issue 1 (2015), pp. 67–87
Poisson conditional autoregressive model of spatio-temporal data is proposed. Markov property and probabilistic characteristics of this model are presented. Algorithms for maximum likelihood estimation of the model parameters are constructed. Optimal forecasting statistic minimizing probability of forecast error is given. The “plug-in” principle based on ML-estimators is used for forecasting in the case of unknown parameters. The results of computer experiments on simulated and real medical data are presented.
Pub. online:1 Jan 2002Type:Research ArticleOpen Access
Volume 13, Issue 2 (2002), pp. 209–226
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.