Locally adaptive spatial smoothing using conditional auto-regressive models

Lee, D. and Mitchell, R. (2013) Locally adaptive spatial smoothing using conditional auto-regressive models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 62(4), pp. 593-608. (doi: 10.1111/rssc.12009)

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Conditional autoregressive (CAR) models are commonly used to capture spatial correlation in areal unit data, as part of a hierarchical Bayesian model. The spatial correlation structure induced by these models is determined by geographical adjacency, but this is too simplistic for some real datasets, which can visually exhibit sub-regions of strong correlation as well as locations at which the response exhibits a step-change. An example of this, and the motivation for this paper, is the spatial pattern in respiratory disease risk in the 271 Intermediate Geographies in the Greater Glasgow and Clyde Health board in 2005, which is displayed in Figure 2. The methodology proposed is an extension to the class of CAR priors, which allow them to capture such localised spatial correlation and identify step changes. The approach takes the form of an iterative algorithm, which sequentially updates the spatial correlation structure assumed by the model in addition to estimating the remaining parameters. The efficacy of the approach is assessed by simulation, before being applied to the motivating Greater Glasgow application.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Mitchell, Professor Rich and Lee, Professor Duncan
Authors: Lee, D., and Mitchell, R.
College/School:College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Public Health
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Journal of the Royal Statistical Society: Series C (Applied Statistics)
Publisher:Royal Statistical Society
ISSN (Online):1467-9876

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
538431Allowing for cliffs and slopes in the risk surface when modelling small-area spatial dataDuncan LeeEconomic & Social Research Council (ESRC)ES/I015604/1M&S - STATISTICS