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December 2015 Restricted Covariance Priors with Applications in Spatial Statistics
Theresa R. Smith, Jon Wakefield, Adrian Dobra
Bayesian Anal. 10(4): 965-990 (December 2015). DOI: 10.1214/14-BA927


We present a Bayesian model for area-level count data that uses Gaussian random effects with a novel type of G-Wishart prior on the inverse variance–covariance matrix. Specifically, we introduce a new distribution called the truncated G-Wishart distribution that has support over precision matrices that lead to positive associations between the random effects of neighboring regions while preserving conditional independence of non-neighboring regions. We describe Markov chain Monte Carlo sampling algorithms for the truncated G-Wishart prior in a disease mapping context and compare our results to Bayesian hierarchical models based on intrinsic autoregression priors. A simulation study illustrates that using the truncated G-Wishart prior improves over the intrinsic autoregressive priors when there are discontinuities in the disease risk surface. The new model is applied to an analysis of cancer incidence data in Washington State.


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Theresa R. Smith. Jon Wakefield. Adrian Dobra. "Restricted Covariance Priors with Applications in Spatial Statistics." Bayesian Anal. 10 (4) 965 - 990, December 2015.


Published: December 2015
First available in Project Euclid: 4 February 2015

zbMATH: 1335.62064
MathSciNet: MR3432246
Digital Object Identifier: 10.1214/14-BA927

Rights: Copyright © 2015 International Society for Bayesian Analysis


Vol.10 • No. 4 • December 2015
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