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Improved inference for areal unit count data using graph-based optimisation
Statistics and computing
AbstractSpatio-temporal count data relating to a set of non-overlapping areal units are prevalent in many fields, including epidemiology and social science. The spatial autocorrelation inherent in these data is typically modelled by a set of random effects that are assigned a conditional autoregressive prior distribution, which is a special case of a Gaussian Markov random field. The autocorrelation structure implied by this model depends on a binary neighbourhood matrix, where two randomdoi:10.1007/s11222-021-10025-7 fatcat:2b5f4puqijewhczpxniqygw63e