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dc.contributor.authorIllian, Janine Baerbel
dc.contributor.authorSoerbye, S
dc.contributor.authorRue, H
dc.contributor.authorHendrichsen, D
dc.identifier.citationIllian , J B , Soerbye , S , Rue , H & Hendrichsen , D 2012 , ' Using INLA to fit a complex point process model with temporally varying effects – a case study ' , Journal of Environmental Statistics , vol. 3 , no. 7 .en
dc.identifier.otherPURE: 5264614
dc.identifier.otherPURE UUID: 72fb0cdc-7829-4e28-9327-2b419a624ed7
dc.description.abstractIntegrated nested Laplace approximation (INLA) provides a fast and yet quite exact approach to fitting complex latent Gaussian models which comprise many statistical models in a Bayesian context, including log Gaussian Cox processes. This paper discusses how a joint log Gaussian Cox process model may be fitted to independent replicated point patterns. We illustrate the approach by fitting a model to data on the locations of muskoxen (Ovibos moschatus) herds in Zackenberg valley, Northeast Greenland and by detailing how this model is specified within the R-interface R-INLA. The paper strongly focuses on practical problems involved in the modelling process, including issues of spatial scale, edge effects and prior choices, and finishes with a discussion on models with varying boundary conditions.
dc.relation.ispartofJournal of Environmental Statisticsen
dc.rights(c) 2012 The authors. This is an open access article available under the terms of the Creative Commons Attribution License ( which permits anyone to download, reuse, reprint, modify, distribute, and/or copy articles in Journal of Environmental Statistics, so long as the original authors and source are credited.en
dc.subjectSpatial point processen
dc.subjectSpatial scaleen
dc.subjectReplicated patternsen
dc.subjectQA Mathematicsen
dc.titleUsing INLA to fit a complex point process model with temporally varying effects – a case studyen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.description.statusPeer revieweden

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