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Activity Number:
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169
- Advanced Bayesian Topics (Part 2)
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Type:
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Contributed
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Date/Time:
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Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #318261
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Title:
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Bayesian Model Selection for Spatial Hierarchical Models with Intrinsic Conditional Autoregressive Priors via Fractional Bayes Factors
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Author(s):
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Erica May Porter* and Christopher Thomas Franck and Marco May Ferreira
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Companies:
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Virginia Tech Department of Statistics and Virginia Tech Department of Statistics and Virginia Tech Department of Statistics
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Keywords:
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spatial statistics;
Bayesian model selection;
ICAR random effects;
areal data
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Abstract:
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We develop Bayesian model selection 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 effects is difficult, as spatial confounding creates a tension between fixed and spatial random effects. Researchers have commonly performed selection separately for fixed and random effects in spatial hierarchical models. Simultaneous selection methods relieve the researcher from arbitrarily fixing one of these types of effects while selecting the other. Notably, Bayesian approaches to simultaneously select covariates and spatial effects are limited. Our use of fractional Bayes factors allows for selection of fixed and random effects 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.
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Authors who are presenting talks have a * after their name.
