Home > Events > 2015 Seminars & Colloquia > Edson Utazi, University of Southampton

Edson Utazi, University of Southampton

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A Bayesian latent process spatiotemporal regression model for areal count data
17 April 2018 from 4:00 PM to 5:00 PM
104 Thomas Building
Contact Name
Lorey Burghard
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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. In this talk, I will discuss 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. Results based on real-life examples show that the proposed approach is at least as effective as CAR-based models.

In addition, he will be discussing some of his work on vaccination coverage mapping and determining the representativeness of health and demographic surveillance systems (HDSS) networks.
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