A distance-based model for spatial prediction using radial basis functions

Carlos E. Melo (), Oscar O. Melo () and Jorge Mateu ()
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Carlos E. Melo: Universidad Distrital Francisco José de Caldas
Oscar O. Melo: Universidad Nacional de Colombia
Jorge Mateu: University Jaume I

AStA Advances in Statistical Analysis, 2018, vol. 102, issue 2, No 7, 263-288

Abstract: 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.

Keywords: Detrending; Distance-based methods; Radial basis functions; Random function models; Smoothing parameter; Spatial prediction (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:alstar:v:102:y:2018:i:2:d:10.1007_s10182-017-0305-4

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DOI: 10.1007/s10182-017-0305-4

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