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dc.contributor.authorIllian, Janine Baerbel
dc.contributor.authorSorbye, S H
dc.contributor.authorRue, H
dc.date.accessioned2011-12-16T09:36:44Z
dc.date.available2011-12-16T09:36:44Z
dc.date.issued2012-12
dc.identifier.citationIllian , J B , Sorbye , S H & Rue , H 2012 , ' A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA) ' , Annals of Applied Statistics , vol. 6 , no. 4 , pp. 1499-1530 . https://doi.org/10.1214/11-AOAS530en
dc.identifier.issn1932-6157
dc.identifier.otherPURE: 5264551
dc.identifier.otherPURE UUID: 028b450e-7740-4562-a8b9-28045169905f
dc.identifier.otherScopus: 84874552697
dc.identifier.urihttp://hdl.handle.net/10023/2120
dc.description"The authors also gratefully acknowledge the financial support of Research Councils UK for Illian"en
dc.description.abstractThis paper develops methodology that provides a toolbox for routinely fitting complex models to realistic spatial point pattern data. We consider models that are based on log-Gaussian Cox processes and include local interaction in these by considering constructed covariates. This enables us to use integrated nested Laplace approximation and to considerably speed up the inferential task. In addition, methods for model comparison and model assessment facilitate the modelling process. The performance of the approach is assessed in a simulation study. To demonstrate the versatility of the approach, models are tted to two rather dierent examples, a large rainforest data set with covariates and a point pattern with multiple marks.
dc.format.extent32
dc.language.isoeng
dc.relation.ispartofAnnals of Applied Statisticsen
dc.rights© Institute of Mathematical Statistics, 2012en
dc.subjectCox processesen
dc.subjectMarked point patternsen
dc.subjectModel assessmenten
dc.subjectModel comparisonen
dc.subjectQA Mathematicsen
dc.subject.lccQAen
dc.titleA toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA)en
dc.typeJournal articleen
dc.description.versionPostprinten
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.identifier.doihttps://doi.org/10.1214/11-AOAS530
dc.description.statusPeer revieweden


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