title: Spatially-dependent Bayesian model selection for disease mapping.
abstract: 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.
authors.affiliation: 1 Department of Public Health, Medical University of South Carolina, Charleston, SC, USA.
authors.forename: Rachel
authors.initials: R
authors.lastname: Carroll
authors.affiliation: 1 Department of Public Health, Medical University of South Carolina, Charleston, SC, USA.
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Lawson
authors.affiliation: 2 Interuniversity Institute for Statistics and statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium.
authors.forename: Christel
authors.initials: C
authors.lastname: Faes
authors.affiliation: 3 Department of Community and Family Health, University of South Florida, Tampa, FL, USA.
authors.forename: Russell S
authors.initials: RS
authors.lastname: Kirby
authors.affiliation: 1 Department of Public Health, Medical University of South Carolina, Charleston, SC, USA.
authors.forename: Mehreteab
authors.initials: M
authors.lastname: Aregay
authors.affiliation: 2 Interuniversity Institute for Statistics and statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium.
authors.forename: Kevin
authors.initials: K
authors.lastname: Watjou
mesh_terms: D001499:Bayes Theorem; D004196:Disease Outbreaks; D004812:Epidemiologic Methods; D005544:Forecasting; D062305:Geographic Mapping; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical
keywords: BRugs; Bayesian model averaging; Bayesian model selection; MCMC; R2WinBUGS; spatial