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• W2104703669 abstract "Statistical models for areal data are primarily used for smoothing maps revealing spatial trends. Subsequent interest often resides in the formal identification of 'boundaries' on the map. Here boundaries refer to 'difference boundaries', representing significant differences between adjacent regions. Recently, Lu and Carlin (2004) discussed a Bayesian framework to carry out edge detection employing a spatial hierarchical model that is estimated using Markov chain Monte Carlo (MCMC) methods. Here we offer an alternative that avoids MCMC and is easier to implement. Our approach resembles a model comparison problem where the models correspond to different underlying edge configurations across which we wish to smooth (or not). We incorporate these edge configurations in spatially autoregressive models and demonstrate how the Bayesian Information Criteria (BIC) can be used to detect difference boundaries in the map. We illustrate our methods with a Minnesota Pneumonia amd Influenza Hospitalization dataset to elicit boundaries detected from the different models." @default.
• W2104703669 created "2016-06-24" @default.
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• W2104703669 date "2010-06-18" @default.
• W2104703669 modified "2023-10-12" @default.
• W2104703669 title "Mining boundary effects in areally referenced spatial data using the Bayesian information criterion" @default.
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• W2104703669 doi "https://doi.org/10.1007/s10707-010-0109-0" @default.
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