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dc.contributor.authorD'ANGELO, Nicoletta
dc.contributor.authoradelfio, giada
dc.contributor.authorMateu, Jorge
dc.date.accessioned2023-04-21T07:07:00Z
dc.date.available2023-04-21T07:07:00Z
dc.date.issued2022-12-09
dc.identifier.citationD'ANGELO, Nicoletta; ADELFIO, Giada; MATEU, Jorge. Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes. Computational Statistics & Data Analysis, 2023, vol. 180, p. 107679.ca_CA
dc.identifier.urihttp://hdl.handle.net/10234/202233
dc.description.abstractA local version of spatio-temporal log-Gaussian Cox processes is proposed by using Local Indicators of Spatio-Temporal Association (LISTA) functions plugged into the minimum contrast procedure, to obtain space as well as time-varying parameters. The new procedure resorts to the joint minimum contrast fitting method to estimate the set of second-order parameters. This approach has the advantage of being suitable in both separable and non-separable parametric specifications of the correlation function of the underlying Gaussian Random Field. Simulation studies to assess the performance of the proposed fitting procedure are presented, and an application to seismic spatio-temporal point pattern data is shown.ca_CA
dc.format.extent15 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherElsevierca_CA
dc.relation.isPartOfComputational Statistics & Data Analysis, 2023, vol. 180ca_CA
dc.rights© 2022 Elsevier B.V. All rights reserved.ca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/ca_CA
dc.subjectlocal modelca_CA
dc.subjectslog-Gaussian cox processesca_CA
dc.subjectminimum contrastca_CA
dc.subjectsecond-order characteristicsca_CA
dc.subjectspatio-temporal point processesca_CA
dc.titleLocally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processesca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1016/j.csda.2022.107679
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.type.versioninfo:eu-repo/semantics/submittedVersionca_CA
project.funder.nameUniversity of Palermo, Italyca_CA
oaire.awardNumberFFR 2021 GIADA ADELFIOca_CA


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