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dc.contributor.authorCongdon, P
dc.date.accessioned2016-09-13T15:25:20Z
dc.date.issued2016-08-03
dc.date.issued2016-08-03
dc.date.issued2016-08-03
dc.date.submitted2016-09-09T10:21:26.841Z
dc.identifier.issn1436-3240
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/15282
dc.description.abstract© 2016 The Author(s)Variation in disease risk underlying observed disease counts is increasingly a focus for Bayesian spatial modelling, including applications in spatial data mining. Bayesian analysis of spatial data, whether for disease or other types of event, often employs a conditionally autoregressive prior, which can express spatial dependence commonly present in underlying risks or rates. Such conditionally autoregressive priors typically assume a normal density and uniform local smoothing for underlying risks. However, normality assumptions may be affected or distorted by heteroscedasticity or spatial outliers. It is also desirable that spatial disease models represent variation that is not attributable to spatial dependence. A spatial prior representing spatial heteroscedasticity within a model accommodating both spatial and non-spatial variation is therefore proposed. Illustrative applications are to human TB incidence. A simulation example is based on mainland US states, while a real data application considers TB incidence in 326 English local authorities.
dc.format.extent1 - 14
dc.language.isoenen_US
dc.relation.ispartofStochastic Environmental Research and Risk Assessment
dc.rightsCC-BY
dc.titleRepresenting spatial dependence and spatial discontinuity in ecological epidemiology: a scale mixture approach
dc.typeJournal Article
dc.rights.holder© 2016 The Author(s)
dc.identifier.doi10.1007/s00477-016-1292-9
dc.relation.isPartOfStochastic Environmental Research and Risk Assessment
dc.relation.isPartOfStochastic Environmental Research and Risk Assessment
pubs.organisational-group/Queen Mary University of London
pubs.organisational-group/Queen Mary University of London/Faculty of Humanities, Social Sciences & Law
pubs.organisational-group/Queen Mary University of London/Faculty of Humanities, Social Sciences & Law/Geography - Staff
pubs.publication-statusAccepted


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