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dc.contributor.authorIllian, Janine Baerbel
dc.contributor.authorSorbye, S H
dc.contributor.authorRue, H
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 .
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.relation.ispartofAnnals of Applied Statisticsen
dc.subjectCox processesen
dc.subjectMarked point patternsen
dc.subjectModel assessmenten
dc.subjectModel comparisonen
dc.subjectQA Mathematicsen
dc.titleA toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA)en
dc.typeJournal articleen
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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