Derives closed-form m* threshold below which spatial random effects materially affect regression inference in multilevel areal data and above which nonspatial models suffice, with O(m^{-1}) convergence in posterior variance differences.
Journal of the Royal Statistical Society: Series B (Methodological) , volume=
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A novel spatially dependent shrinkage prior for Poisson regression improves region selection and prediction accuracy for count data with spatially correlated covariates.
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On the Need for Spatial Random Effects in Bayesian Regression Models for Multilevel Areal Data
Derives closed-form m* threshold below which spatial random effects materially affect regression inference in multilevel areal data and above which nonspatial models suffice, with O(m^{-1}) convergence in posterior variance differences.
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Bayesian Region Selection and Prediction in Poisson Regression with Spatially Dependent Global-Local Shrinkage Prior
A novel spatially dependent shrinkage prior for Poisson regression improves region selection and prediction accuracy for count data with spatially correlated covariates.
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