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A distance-based model for spatial prediction using radial basis functions
dc.contributor.author | Melo, Carlos E. | |
dc.contributor.author | Melo, Oscar O. | |
dc.contributor.author | Mateu, Jorge | |
dc.date.accessioned | 2018-06-20T15:51:41Z | |
dc.date.available | 2018-06-20T15:51:41Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | MELO, 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. 1-26 | ca_CA |
dc.identifier.issn | 1863-8171 | |
dc.identifier.issn | 1863-818X | |
dc.identifier.uri | http://hdl.handle.net/10234/175277 | |
dc.description.abstract | In 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.extent | 26 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Springer Berlin Heidelberg | ca_CA |
dc.relation.isPartOf | AStA Advances in Statistical Analysis, 2017, vol. 102, núm, 2, p. 1-26 | ca_CA |
dc.rights | © Springer-Verlag GmbH Germany 2017 | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | detrending | ca_CA |
dc.subject | distance-based methods | ca_CA |
dc.subject | radial basis functions | ca_CA |
dc.subject | random function models | ca_CA |
dc.subject | smoothing parameter | ca_CA |
dc.subject | spatial prediction | ca_CA |
dc.title | A distance-based model for spatial prediction using radial basis functions | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1007/s10182-017-0305-4 | |
dc.relation.projectID | Work 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.accessRights | info:eu-repo/semantics/restrictedAccess | ca_CA |
dc.relation.publisherVersion | https://link.springer.com/article/10.1007/s10182-017-0305-4 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
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