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dc.contributor.authorMelo, Carlos E.
dc.contributor.authorMelo, Oscar O.
dc.contributor.authorMateu, Jorge
dc.date.accessioned2018-06-20T15:51:41Z
dc.date.available2018-06-20T15:51:41Z
dc.date.issued2018
dc.identifier.citationMELO, Carlos E.; MELO, Oscar O.; MATEU, Jorge. A distance-based model for spatial prediction using radial basis functions. AStA Advances in Statistical Analysis, 2017, vol. 102, núm, 2, p. 1-26ca_CA
dc.identifier.issn1863-8171
dc.identifier.issn1863-818X
dc.identifier.urihttp://hdl.handle.net/10234/175277
dc.description.abstractIn the context of local interpolators, radial basis functions (RBFs) are known to reduce the computational time by using a subset of the data for prediction purposes. In this paper, we propose a new distance-based spatial RBFs method which allows modeling spatial continuous random variables. The trend is incorporated into a RBF according to a detrending procedure with mixed variables, among which we may have categorical variables. In order to evaluate the efficiency of the proposed method, a simulation study is carried out for a variety of practical scenarios for five distinct RBFs, incorporating principal coordinates. Finally, the proposed method is illustrated with an application of prediction of calcium concentration measured at a depth of 0–20 cm in Brazil, selecting the smoothing parameter by cross-validation.ca_CA
dc.format.extent26 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringer Berlin Heidelbergca_CA
dc.relation.isPartOfAStA Advances in Statistical Analysis, 2017, vol. 102, núm, 2, p. 1-26ca_CA
dc.rights© Springer-Verlag GmbH Germany 2017ca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/*
dc.subjectdetrendingca_CA
dc.subjectdistance-based methodsca_CA
dc.subjectradial basis functionsca_CA
dc.subjectrandom function modelsca_CA
dc.subjectsmoothing parameterca_CA
dc.subjectspatial predictionca_CA
dc.titleA distance-based model for spatial prediction using radial basis functionsca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1007/s10182-017-0305-4
dc.relation.projectIDWork partially funded and supported by: Grant MTM2016-78917-R from the Spanish Ministry of Science and Education; Core Spatial Data Research (Faculty of Engineering, Francisco José de Caldas District University) (Grant COL0013969); and Applied Statistics in Experimental Research, Industry and Biotechnology (Universidad Nacional de Colombia) (Grant COL0004469).ca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccessca_CA
dc.relation.publisherVersionhttps://link.springer.com/article/10.1007/s10182-017-0305-4ca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA


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