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.

Lagona, F., Ranalli, M., Barbi, E. (2020). A model with space-varying regression coefficients for clustering multivariate spatial count data. BIOMETRICAL JOURNAL, 62(6), 1508-1524 [10.1002/bimj.201900229].

A model with space-varying regression coefficients for clustering multivariate spatial count data

francesco lagona;monia ranalli;
2020

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.
Lagona, F., Ranalli, M., Barbi, E. (2020). A model with space-varying regression coefficients for clustering multivariate spatial count data. BIOMETRICAL JOURNAL, 62(6), 1508-1524 [10.1002/bimj.201900229].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/359972
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