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dc.contributor.authorHildeman, Anders
dc.contributor.authorBolin, David
dc.contributor.authorWallin, Jonas
dc.contributor.authorIllian, Janine B.
dc.date.accessioned2019-04-03T23:37:48Z
dc.date.available2019-04-03T23:37:48Z
dc.date.issued2018-12
dc.identifier.citationHildeman , A , Bolin , D , Wallin , J & Illian , J B 2018 , ' Level set Cox processes ' , Spatial Statistics , vol. 28 , pp. 169-193 . https://doi.org/10.1016/j.spasta.2018.03.004en
dc.identifier.issn2211-6753
dc.identifier.otherPURE: 252545552
dc.identifier.otherPURE UUID: 5c33f69c-4a64-4a5a-9ece-9e503cf7d1ff
dc.identifier.otherScopus: 85045031909
dc.identifier.otherWOS: 000451116200012
dc.identifier.urihttp://hdl.handle.net/10023/17433
dc.descriptionThe authors gratefully acknowledge the financial support from the Knut and Alice Wallenberg Foundation, the Swedish Research Council Grant 2016-04187, and the ÅForsk foundation. Numerous organizations have provided funding, principally the U.S. National Science Foundation, and hundreds of field workers have contributed. The Barro Colorado soil survey was funded by NSFDEB021104, 021115, 0212284, 0212818 and OISE0314581 as well as the STRI Soils Initiative and CTFS.en
dc.description.abstractAn extension of the popular log-Gaussian Cox process (LGCP) model for spatial point patterns is proposed for data exhibiting fundamentally different behaviors in different subregions of the spatial domain. The aim of the analyst might be either to identify and classify these regions, to perform kriging, or to derive some properties of the parameters driving the random field in one or several of the subregions. The extension is based on replacing the latent Gaussian random field in the LGCP by a latent spatial mixture model specified using a categorically valued random field. This classification is defined through level set operations on a Gaussian random field and allows for standard stationary covariance structures, such as the Matérn family, to be used to model random fields with some degree of general smoothness but also occasional and structured sharp discontinuities. A computationally efficient MCMC method is proposed for Bayesian inference and we show consistency of finite dimensional approximations of the model. Finally, the model is fitted to point pattern data derived from a tropical rainforest on Barro Colorado island, Panama. We show that the proposed model is able to capture behavior for which inference based on the standard LGCP is biased.
dc.language.isoeng
dc.relation.ispartofSpatial Statisticsen
dc.rights© 2018 Elsevier Ltd. 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/https://doi.org/10.1016/j.spasta.2018.03.004en
dc.subjectPoint processen
dc.subjectCox processen
dc.subjectLevel set inversionen
dc.subjectClassificationen
dc.subjectGaussian fieldsen
dc.subjectQA Mathematicsen
dc.subjectNDASen
dc.subjectBDCen
dc.subjectR2Cen
dc.subject.lccQAen
dc.titleLevel set Cox processesen
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/10.1016/j.spasta.2018.03.004
dc.description.statusPeer revieweden
dc.date.embargoedUntil2019-04-04


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