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

Melo Carlos E.; Melo Oscar O.; Mateu Jorge

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

机译：使用径向基函数的基于距离的空间预测模型

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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.

机译：在局部插值器的上下文中，已知径向基函数（RBF）通过使用数据子集进行预测来减少计算时间。在本文中，我们提出了一种新的基于距离的空间RBFs方法，该方法可以对空间连续随机变量进行建模。根据带有混合变量的去趋势程序，将趋势合并到RBF中，其中可能包含分类变量。为了评估所提出方法的效率，针对五种不同的RBF，结合了主坐标，针对各种实际情况进行了仿真研究。最后，通过预测在巴西0-20 cm深度处测得的钙浓度，并通过交叉验证选择平滑参数，说明了所提出的方法。

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《Advances in statistical analysis》 |2018年第2期|263-288|共26页
作者
Melo Carlos E.; Melo Oscar O.; Mateu Jorge;
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作者单位

Univ Dist Francisco Jose de Caldas, Fac Engn, Bogota, Colombia;

Univ Nacl Colombia, Dept Stat, Fac Sci, Crr 30 45-03, Bogota, Colombia;

Univ Jaume 1, Dept Math, Campus Riu Sec, Castellon De La Plana 12071, Castellon, Spain;

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正文语种 eng
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关键词
Detrending; Distance-based methods; Radial basis functions; Random function models; Smoothing parameter; Spatial prediction;

机译：去趋势;基于距离的方法;径向基函数;随机函数模型;平滑参数;空间预测;

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

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