A distance-based model for spatial prediction using radial basis functions
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https://doi.org/10.1007/s10182-017-0305-4 |
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Título
A distance-based model for spatial prediction using radial basis functionsFecha de publicación
2018Editor
Springer Berlin HeidelbergISSN
1863-8171; 1863-818XCita bibliográfica
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. 1-26Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s10182-017-0305-4Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
AStA Advances in Statistical Analysis, 2017, vol. 102, núm, 2, p. 1-26Proyecto de investigación
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).Derechos de acceso
© Springer-Verlag GmbH Germany 2017
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