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Title: Objective Bayesian model selection for spatial hierarchical models with intrinsic conditional autoregressive priors Authors:  Erica Porter - Virginia Tech Department of Statistics (United States)
Christopher Franck - Virginia Tech (United States)
Marco Ferreira - Virginia Tech (United States) [presenting]
Abstract: Bayesian model selection is developed via fractional Bayes factors to simultaneously assess spatial dependence and select regressors in Gaussian hierarchical models with intrinsic conditional autoregressive (ICAR) spatial random effects. Selection of covariates and spatial model structure is difficult, as spatial confounding creates a tension between fixed and spatial random effects. The use of fractional Bayes factors allows for the selection of fixed effects and spatial model structure under automatic reference priors for model parameters, which obviates the need to specify hyperparameters for priors. We also derive the minimal training size for the fractional Bayes factor applied to the ICAR model under the reference prior. We perform a simulation study to assess the performance of our approach and we compare results to other readily available methods. We demonstrate that our fractional Bayes factor approach assigns a low posterior model probability to spatial models when data is truly independent and reliably selects the correct covariate structure with the highest probability within the model space. Finally, we demonstrate our Bayesian model selection approach with applications to county-level median household income in the contiguous United States and residential crime rates in the neighborhoods of Columbus, Ohio.