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Finite Mixture Models for Mapping Spatially Dependent Disease Counts
Author(s) -
Alfó Marco,
Nieddu Luciano,
Vicari Donatella
Publication year - 2009
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200810494
Subject(s) - univariate , random effects model , representation (politics) , statistics , parametric statistics , mathematics , parametric model , set (abstract data type) , gibbs sampling , mixture model , computer science , econometrics , statistical physics , algorithm , multivariate statistics , meta analysis , bayesian probability , medicine , physics , politics , political science , law , programming language
A vast literature has recently been concerned with the analysis of variation in disease counts recorded across geographical areas with the aim of detecting clusters of regions with homogeneous behavior. Most of the proposed modeling approaches have been discussed for the univariate case and only very recently spatial models have been extended to predict more than one outcome simultaneously. In this paper we extend the standard finite mixture models to the analysis of multiple, spatially correlated, counts. Dependence among outcomes is modeled using a set of correlated random effects and estimation is carried out by numerical integration through an EM algorithm without assuming any specific parametric distribution for the random effects. The spatial structure is captured by the use of a Gibbs representation for the prior probabilities of component membership through a Strauss‐like model. The proposed model is illustrated using real data (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)