Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/23872 
Year of Publication: 
2003
Series/Report no.: 
Discussion Paper No. 314
Publisher: 
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, München
Abstract: 
We propose a full model-based framework for a statistical analysis of incidence or mortality count data stratified by age, period and space, with specific inclusion of additional cohort effects. The setup will be fully Bayesian based on a series of Gaussian Markov random field priors for each of the components. Additional space-time interactions will be either modelled as space-period or space-cohort effects. Statistical inference is based on efficient algorithms to block update Gaussian Markov random fields, which have recently been proposed in the literature. We illustrate our approach in an analysis of stomach cancer data in West Germany.
Subjects: 
Block updating
disease mapping
hierarchical models
Markov chain Monte Carlo
Markov random field models
age-period-cohort model
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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