A Bayesian latent process spatiotemporal regression model for areal count data

Utazi, C. Edson and Afuecheta, Emmanuel O. and Nnanatu, Chibuzor (2018) A Bayesian latent process spatiotemporal regression model for areal count data. Spatial and Spatio-temporal Epidemiology, 25. pp. 25-37. ISSN 1877-5845

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Abstract Model-based approaches for the analysis of areal count data are commonplace in spatiotemporal analysis. In Bayesian hierarchical models, a latent process is incorporated in the mean function to account for dependence in space and time. Typically, the latent process is modelled using a conditional autoregressive (CAR) prior. The aim of this paper is to offer an alternative approach to CAR-based priors for modelling the latent process. The proposed approach is based on a spatiotemporal generalization of a latent process Poisson regression model developed in a time series setting. Spatiotemporal dependence in the autoregressive model for the latent process is modelled through its transition matrix, with a structured covariance matrix specified for its error term. The proposed model and its parameterizations are fitted in a Bayesian framework implemented via MCMC techniques. Our findings based on real-life examples show that the proposed approach is at least as effective as CAR-based models.

Item Type:
Journal Article
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Spatial and Spatio-temporal Epidemiology
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This is the author’s version of a work that was accepted for publication in Spatial and Spatio-temporal Epidemiology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial and Spatio-temporal Epidemiology, 25, 2018 DOI: 10.1016/j.sste.2018.01.003
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20 Feb 2018 17:48
Last Modified:
29 Nov 2022 00:57