Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference

Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference

Abstract Background When analysing spatial data, it is important to account for spatial autocorrelation. In Bayesian statistics, spatial autocorrelation is commonly modelled by the intrinsic conditional autoregressive prior distribution. At the heart of this model is a spatial weights matrix which c...

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Bibliographic Details
Main Authors: Earl W. Duncan, Nicole M. White, Kerrie Mengersen
Format: Article
Language:English
Published: BMC 2017-12-01
Series:International Journal of Health Geographics
Subjects:
Bayesian inference
Spatial smoothing
Spatial autocorrelation
Spatial weights matrix
Conditional autoregressive model
Markov random field
Online Access:http://link.springer.com/article/10.1186/s12942-017-0120-x

Internet

http://link.springer.com/article/10.1186/s12942-017-0120-x

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