A distance-based model for spatial prediction using radial basis functions
| Autor: | Oscar O. Melo, Jorge Mateu, Carlos E. Melo |
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| Předmět: |
Statistics and Probability
Economics and Econometrics distance-based methods Context (language use) 01 natural sciences 010104 statistics & probability random function models spatial prediction Statistics Mixed variables Radial basis function 0101 mathematics Categorical variable Mathematics smoothing parameter Applied Mathematics radial basis functions 010101 applied mathematics Modeling and Simulation Spatial prediction Algorithm Random variable Social Sciences (miscellaneous) Analysis Smoothing detrending Distance based |
| Zdroj: | BASE-Bielefeld Academic Search Engine |
| DOI: | 10.1007/s10182-017-0305-4 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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