Bayesian model averaging, Bayesian model selection, spatial, R2WinBUGS, BRugs, MCMC
Digital Object Identifier (DOI)
In disease mapping where predictor effects are to be modeled, it is often the case that sets of predictors are fixed, and the aim is to choose between fixed model sets. Model selection methods, both Bayesian model selection and Bayesian model averaging, are approaches within the Bayesian paradigm for achieving this aim. In the spatial context, model selection could have a spatial component in the sense that some models may be more appropriate for certain areas of a study region than others. In this work, we examine the use of spatially referenced Bayesian model averaging and Bayesian model selection via a large-scale simulation study accompanied by a small-scale case study. Our results suggest that BMS performs well when a strong regression signature is found.
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Citation / Publisher Attribution
Statistical Methods in Medical Research, v. 27, issue 1, p. 250-268
Carroll, R., Lawson A. B., Faes, C., Kirby, R. S., Aregay, M., & Watjou, K., Spatially-dependent Bayesian Model Selection for Disease Mapping. Statistical Methods in Medical Research, 27(1), pp. 250-268. Copyright © 2018 by SAGE Publications.
The final authenticated version is available online at: https://doi.org/10.1177/0962280215627298.
Scholar Commons Citation
Carroll, Rachel; Lawson, Andrew B.; Faes, Christel; Kirby, Russell S.; Aregay, Mehreteab; and Watjou, Kevin, "Spatially-dependent Bayesian Model Selection for Disease Mapping" (2018). Community and Family Health Faculty Publications. 47.