Show simple item record

Files in this item

Thumbnail

Item metadata

dc.contributor.authorSørbye, Sigrunn
dc.contributor.authorIllian, Janine B.
dc.contributor.authorSimpson, Daniel P.
dc.contributor.authorBurlsem, David
dc.contributor.authorRue, Håvard
dc.date.accessioned2019-11-02T00:36:36Z
dc.date.available2019-11-02T00:36:36Z
dc.date.issued2019-04
dc.identifier.citationSørbye , S , Illian , J B , Simpson , D P , Burlsem , D & Rue , H 2019 , ' Careful prior specification avoids incautious inference for log-Gaussian Cox point processes ' , Journal of the Royal Statistical Society: Series C (Applied Statistics) , vol. 68 , no. 3 , pp. 543-564 . https://doi.org/10.1111/rssc.12321en
dc.identifier.issn0035-9254
dc.identifier.otherPURE: 256025263
dc.identifier.otherPURE UUID: 238d7d64-4a5b-4056-85d9-d3ff2c186afe
dc.identifier.otherScopus: 85055945954
dc.identifier.otherWOS: 000459825100003
dc.identifier.urihttps://hdl.handle.net/10023/18829
dc.descriptionWe acknowledge the principal investigators who were responsible for collecting and analysing the soil maps (Jim Dallin, Robert John, Kyle Harms, Robert Stallard and Joe Yavitt), the funding sources (National Science Foundation grants DEB021104, 021115, 0212284 and 0212818 and Office of International Science and Engineering grant 0314581, the Smithsonian Tropical Research Institute soils initiative and the Center for Tropical Forest Science) and field assistants (Paolo Segre and Juan Di Trani).en
dc.description.abstractHyperprior specifications for random fields in spatial point process modelling can have a major impact on the results. In fitting log-Gaussian Cox processes to rainforest tree species, we consider a reparameterised model combining a spatially structured and an unstructured random field into a single component. This component has one hyperpa- rameter accounting for marginal variance, while an additional hyperparameter governs the fraction of the variance explained by the spatially structured effect. This facilitates inter- pretation of the hyperparameters and significance of covariates is studied for a range of hyperprior specifications. Appropriate scaling makes the analysis invariant to grid resolution.
dc.language.isoeng
dc.relation.ispartofJournal of the Royal Statistical Society: Series C (Applied Statistics)en
dc.rightsCopyright © 2018 Royal Statistical Society. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at: https://doi.org/10.1111/rssc.12321en
dc.subjectBayesian analysisen
dc.subjectSpatial point processen
dc.subjectPenalized complexity prioren
dc.subjectR-INLAen
dc.subjectSpatial modellingen
dc.subjectQA Mathematicsen
dc.subject3rd-DASen
dc.subject.lccQAen
dc.titleCareful prior specification avoids incautious inference for log-Gaussian Cox point processesen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews. 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/10.1111/rssc.12321
dc.description.statusPeer revieweden
dc.date.embargoedUntil2019-11-02


This item appears in the following Collection(s)

Show simple item record