Geoestadística en datos circulares

dc.contributor.advisorGiraldo Henao, Ramónspa
dc.contributor.authorNiño Chaparro, Alejandrospa
dc.date.accessioned2024-05-27T22:58:41Z
dc.date.available2024-05-27T22:58:41Z
dc.date.issued2023
dc.descriptionilustraciones, diagramasspa
dc.description.abstractSe propone una nueva metodología en el contexto de geostadística no estacionaria que permite hacer predicción de datos circulares empleando kriging circular residual cuando la tendencia espacial es modelada a través de redes neuronales. Usando datos simulados y reales (tomados del proyecto NASA power) se hace comparación de la técnica propuesta con pulimento de medianas. Los resultados indican que la estrategia considerada mejora las predicciones. (Texto tomado de la fuente).spa
dc.description.abstractWe propose a new methodology in the context of nonstationary geostatistics that allows the prediction of circular data using residual circular kriging when the spatial trend is modeled through neural networks. Using simulated and real data (taken from the NASA power project), the proposed technique is compared with those obtained through median polish. The results indicate that the strategy proposed improves the predictionseng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ciencias - Estadísticaspa
dc.format.extentix, 58 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/86169
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.facultyFacultad de Cienciasspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ciencias - Maestría en Ciencias - Estadísticaspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/spa
dc.subject.ddc000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresspa
dc.subject.ddc510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasspa
dc.subject.proposalDatos circularesspa
dc.subject.proposalGeoestadística no estacionariaspa
dc.subject.proposalKriging circulareng
dc.subject.proposalPulimento de medianasspa
dc.subject.proposalRedes neuronalesspa
dc.subject.proposalCircular krigingeng
dc.subject.proposalNonstationary geostatisticseng
dc.subject.proposalNeural networkseng
dc.subject.proposalMedian polisheng
dc.subject.proposalDirectional dataeng
dc.subject.wikidatageoestadísticaspa
dc.subject.wikidatageostatisticseng
dc.subject.wikidatageoprocesamientospa
dc.subject.wikidatageoprocessingeng
dc.subject.wikidatared neuronal artificialspa
dc.subject.wikidataartificial neural networkeng
dc.titleGeoestadística en datos circularesspa
dc.title.translatedGeostatistics in circular dataeng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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