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dc.contributor.authorEckardt, Matthias
dc.contributor.authorMateu, Jorge
dc.date.accessioned2018-05-07T18:45:38Z
dc.date.available2018-05-07T18:45:38Z
dc.date.issued2017
dc.identifier.citationECKARDT, Matthias; MATEU, Jorge. Analysing highly complex and highly structured point patterns in space. Spatial Statistics, 2017ca_CA
dc.identifier.issn2211-6753
dc.identifier.urihttp://hdl.handle.net/10234/174538
dc.description.abstractThe purpose of this paper is to discuss two recently introduced approaches that focus on structures of different events that occur randomly in space: spatial dependence graph models (SDGMs), and network intensity functions. While SDGMs are undirected graphical models which capture the conditional independence structure between components of multivariate spatial point processes, network intensity functions describe the first-order properties of point patterns that occur on arbitrary network structures.ca_CA
dc.format.extent10 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherElsevierca_CA
dc.relation.isPartOfSpatial Statistics, 2017ca_CA
dc.rights© 2017 Elsevier B.V. All rights reserved.ca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/*
dc.subjecthuge dataca_CA
dc.subjectintensity predictionca_CA
dc.subjectmultivariate spatial point patternca_CA
dc.subjectnetwork regressionca_CA
dc.titleAnalysing highly complex and highly structured point patterns in spaceca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1016/j.spasta.2017.04.007
dc.relation.projectIDJ. Mateu has been partially granted by grants MTM2013-43917-P, MTM2016-78917-R and P1-1B2015-40.ca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccessca_CA
dc.relation.publisherVersionhttps://www.sciencedirect.com/science/article/pii/S2211675316301269ca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA


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