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dc.contributor.authorSiino, Marianna
dc.contributor.authorAdelfio, Giada
dc.contributor.authorMateu, Jorge
dc.date.accessioned2019-02-13T16:22:20Z
dc.date.available2019-02-13T16:22:20Z
dc.date.issued2018
dc.identifier.citationSiino, Marianna; Adelfio, Giada; Mateu, Jorge. "Joint second-order parameter estimation for spatio-temporal log-Gaussian Cox processes." Stochastic Environmental Research and Risk Assessment, 2018, vol. 32, núm. 12, p. 3525-3539ca_CA
dc.identifier.issn1436-3240
dc.identifier.issn1436-3259
dc.identifier.urihttp://hdl.handle.net/10234/181066
dc.description.abstractWe propose a new fitting method to estimate the set of second-order parameters for the class of homogeneous spatio-temporal log-Gaussian Cox point processes. With simulations, we show that the proposed minimum contrast procedure,based on the spatio-temporal pair correlation function, provides reliable estimates and we compare the results with thecurrent available methods. Moreover, the proposed method can be used in the case of both separable and non-separableparametric specifications of the correlation function of the underlying Gaussian Random Field. We describe earthquakesequences comparing several Cox model specifications.ca_CA
dc.format.extent15 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringerca_CA
dc.relation.isPartOfStochastic Environmental Research and Risk Assessment, 2018, vol. 32, núm. 12, p. 3525-3539ca_CA
dc.rights© Springer-Verlag GmbH Germany, part of Springer Nature 2018ca_CA
dc.subjectearthquakesca_CA
dc.subjectlog-Gaussian Cox processesca_CA
dc.subjectminimum contrast methodca_CA
dc.subjectnon-separable covariance functionca_CA
dc.subjectspatio-temporal pair correlation functionca_CA
dc.titleJoint second-order parameter estimation for spatio-temporal log-Gaussian Cox processesca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1007/s00477-018-1579-0
dc.relation.projectIDThis paper has been supported by the national grant of the Italian Ministry of Education University and Research (MIUR) for the PRIN-2015 program (Progetti di ricerca di Rilevante Interesse Nazionale), “Prot. 20157PRZC4— Research Project Title Complex space-time modeling and functional analysis for probabilistic forecast of seismic events. PI: Giada Adelfio”. J. Mateu has been partially founded by Grants P1-1B2015-40 and MTM2016-78917-Rca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccessca_CA
dc.relation.publisherVersionhttps://link.springer.com/article/10.1007/s00477-018-1579-0ca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA


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