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dc.contributor.authorFreni-Sterrantino, Anna
dc.contributor.authorVentrucci, Massimo
dc.contributor.authorRue, Haavard
dc.date.accessioned2018-05-30T10:50:26Z
dc.date.available2017-12-28T07:32:10Z
dc.date.available2018-05-30T10:50:26Z
dc.date.issued2018-05-23
dc.identifier.citationFreni-Sterrantino A, Ventrucci M, Rue H (2018) A note on intrinsic conditional autoregressive models for disconnected graphs. Spatial and Spatio-temporal Epidemiology 26: 25–34. Available: http://dx.doi.org/10.1016/j.sste.2018.04.002.
dc.identifier.issn1877-5845
dc.identifier.doi10.1016/j.sste.2018.04.002
dc.identifier.urihttp://hdl.handle.net/10754/626456
dc.description.abstractIn this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models should be defined, scaled and implemented. We show how these suggestions can be implemented in two examples, on disease mapping.
dc.description.sponsorshipWe thank Dr M. A. Vigotti (University of Pisa) for having made available the dataset from the Tuscany Atlas of Mortality 1971–1994. Massimo Ventrucci is supported by the PRIN 2015 grant project n.20154X8K23 (EPHASTAT) founded by the Italian Ministry for Education, University and Research. We can provide the code for the examples.
dc.language.isoen
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S1877584517301600
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Spatial and Spatio-temporal Epidemiology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial and Spatio-temporal Epidemiology, [26, (2018)] DOI: 10.1016/j.sste.2018.04.002. © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCAR models
dc.subjectDisease mapping
dc.subjectDisconnected graph
dc.subjectGaussian Markov random fields
dc.subjectIslands
dc.subjectINLA
dc.titleA note on intrinsic conditional autoregressive models for disconnected graphs
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalSpatial and Spatio-temporal Epidemiology
dc.eprint.versionPost-print
dc.contributor.institutionSmall Area Health Statistics Unit, Department of Epidemiology and Biostatistics, Imperial College London, United Kingdom
dc.contributor.institutionDepartment of Statistics, University of Bologna, Bologna, Italy
dc.identifier.arxividarXiv:1705.04854
kaust.personRue, Haavard
dc.versionv1
dc.date.published-online2018-05-23
dc.date.published-print2018-08
dc.date.posted2017-05-13


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NOTICE: this is the author’s version of a work that was accepted for publication in Spatial and Spatio-temporal Epidemiology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial and Spatio-temporal Epidemiology, [26, (2018)] DOI: 10.1016/j.sste.2018.04.002. © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Except where otherwise noted, this item's license is described as NOTICE: this is the author’s version of a work that was accepted for publication in Spatial and Spatio-temporal Epidemiology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial and Spatio-temporal Epidemiology, [26, (2018)] DOI: 10.1016/j.sste.2018.04.002. © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
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