Nonparametric geostatistical risk mapping

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UDC.coleccionInvestigaciónes_ES
UDC.departamentoMatemáticases_ES
UDC.grupoInvModelización, Optimización e Inferencia Estatística (MODES)es_ES
dc.contributor.authorFernández-Casal, Rubén
dc.contributor.authorCastillo-Páez, Sergio
dc.contributor.authorFrancisco-Fernández, Mario
dc.date.accessioned2023-11-23T17:12:39Z
dc.date.available2023-11-23T17:12:39Z
dc.date.issued2018
dc.descriptionVersión final aceptada de: https://doi.org/10.1007/s00477-017-1407-yes_ES
dc.descriptionThis version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00477-017-1407-yes_ES
dc.description.abstractIn this work, a fully nonparametric geostatistical approach to estimate threshold exceeding probabilities is proposed. To estimate the large-scale variability (spatial trend) of the process, the nonparametric local linear regression estimator, with the bandwidth selected by a method that takes the spatial dependence into account, is used. A bias-corrected nonparametric estimator of the variogram, obtained from the nonparametric residuals, is proposed to estimate the small-scale variability. Finally, a bootstrap algorithm is designed to estimate the unconditional probabilities of exceeding a threshold value at any location. The behavior of this approach is evaluated through simulation and with an application to a real data set.es_ES
dc.description.sponsorshipThe research of Rubén Fernández-Casal and Mario Francisco-Fernández has been partially supported by the Consellería de Cultura, Educación e Ordenación Universitaria of the Xunta de Galicia through the agreement for the Singular Research Center CITIC, and by Grant MTM2014-52876-R. The research of Sergio Castillo has been partially supported by the Universidad de las Fuerzas Armadas ESPE, from Ecuador. The authors thank the associate editor and two referees for constructive comments that improved the presentation of this article.es_ES
dc.identifier.citationFernández-Casal, R., Castillo-Páez, S. & Francisco-Fernández, M. Nonparametric geostatistical risk mapping. Stoch Environ Res Risk Assess 32, 675–684 (2018). https://doi.org/10.1007/s00477-017-1407-yes_ES
dc.identifier.doi10.1007/s00477-017-1407-y
dc.identifier.urihttp://hdl.handle.net/2183/34323
dc.language.isoenges_ES
dc.relation.isversionofhttps://doi.org/10.1007/s00477-017-1407-y
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2014-52876-R/ES/INFERENCIA ESTADISTICA COMPLEJA Y DE ALTA DIMENSION: EN GENOMICA, NEUROCIENCIA, ONCOLOGIA, MATERIALES COMPLEJOS, MALHERBOLOGIA, MEDIO AMBIENTE, ENERGIA Y APLICACIONES INDUSTRIes_ES
dc.relation.urihttps://link.springer.com/article/10.1007/s00477-017-1407-yes_ES
dc.rightsTodos os dereitos reservados. All rights reserved.es_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectLocal linear regressiones_ES
dc.subjectNonparametric estimationes_ES
dc.subjectKriginges_ES
dc.subjectBias-corrected variogram estimationes_ES
dc.subjectBootstrapes_ES
dc.titleNonparametric geostatistical risk mappinges_ES
dc.typejournal articlees_ES
dspace.entity.typePublication
relation.isAuthorOfPublication96b3567f-5599-4789-bdfe-e621516d18ef
relation.isAuthorOfPublication9724fb7a-c0db-4b2f-aa1a-7f79bf9c2064
relation.isAuthorOfPublication.latestForDiscovery96b3567f-5599-4789-bdfe-e621516d18ef

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