Introduces conditional autoregressive models for spatially dependent functional data with consistent covariance estimation via conditional centering and superconsistent, asymptotically normal estimation of the spatial dependence parameter under an expanding lattice.
Journal of the Royal Statistical Society: Series B (Methodological) , volume=
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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.
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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A new class of functional conditional autoregressive models
Introduces conditional autoregressive models for spatially dependent functional data with consistent covariance estimation via conditional centering and superconsistent, asymptotically normal estimation of the spatial dependence parameter under an expanding lattice.
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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.