A Bayesian latent process spatiotemporal regression model for areal count data. (June 2018)

• Record Type: Journal Article
• Authors: Utazi, C. Edson; Afuecheta, Emmanuel O.; Nnanatu, C. Christopher
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 25(2018)
• Page Start: 25
• Page End: 37
• Publication Date: 2018-06
• Subjects: Autoregressive latent process -- Bayesian inference -- Conditional autoregressive prior -- Markov chain Monte Carlo -- Spatiotemporal areal count data; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2018.01.003
• Languages: English
• ISSNs: 1877-5845

Abstract

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.