Improved inference for areal unit count data using graph-based optimisation

Lee, D. , Meeks, K. and Pettersson, W. (2021) Improved inference for areal unit count data using graph-based optimisation. Statistics and Computing, 31(4), 51. (doi: 10.1007/s11222-021-10025-7)

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Abstract

Spatio-temporal count data relating to a set of non-overlapping areal units are prevalent in many fields, including epidemiology and social science. The spatial autocorrelation inherent in these data is typically modelled by a set of random effects that are assigned a conditional autoregressive prior distribution, which is a special case of a Gaussian Markov random field. The autocorrelation structure implied by this model depends on a binary neighbourhood matrix, where two random effects are assumed to be partially autocorrelated if their areal units share a common border, and are conditionally independent otherwise. This paper proposes a novel graph-based optimisation algorithm for estimating either a static or a temporally varying neighbourhood matrix for the data that better represents its spatial correlation structure, by viewing the areal units as the vertices of a graph and the neighbour relations as the set of edges. The improved estimation performance of our methodology compared to the commonly used border sharing rule is evidenced by simulation, before the method is applied to a new respiratory disease surveillance study in Scotland between 2011 and 2017.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Lee, Professor Duncan and Pettersson, Dr William and Meeks, Dr Kitty
Authors: Lee, D., Meeks, K., and Pettersson, W.
College/School:College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Statistics and Computing
Publisher:Springer
ISSN:0960-3174
ISSN (Online):1573-1375
Published Online:29 June 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Statistics and Computing 31(4): 51
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
305944Multilayer Algorithmics to Leverage Graph StructureKitty MeeksEngineering and Physical Sciences Research Council (EPSRC)EP/T004878/1M&S - Statistics