A model with space-varying regression coefficients for clustering multivariate spatial count data |
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Authors: | Francesco Lagona Monia Ranalli Elisabetta Barbi |
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Affiliation: | 1. Department of Political Sciences, Roma Tre University, Rome, Italy;2. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy |
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Abstract: | Multivariate spatial count data are often segmented by unobserved space-varying factors that vary across space. In this setting, regression models that assume space-constant covariate effects could be too restrictive. Motivated by the analysis of cause-specific mortality data, we propose to estimate space-varying effects by exploiting a multivariate hidden Markov field. It models the data by a battery of Poisson regressions with spatially correlated regression coefficients, which are driven by an unobserved spatial multinomial process. It parsimoniously describes multivariate count data by means of a finite number of latent classes. Parameter estimation is carried out by composite likelihood methods, that we specifically develop for the proposed model. In a case study of cause-specific mortality data in Italy, the model was capable to capture the spatial variation of gender differences and age effects. |
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Keywords: | cause-specific mortality composite likelihood hidden Markov field model-based clustering Potts model |
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