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In various scientific fields properties are represented by functions varying over space. In this paper, we present a methodology to make spatial predictions at non-data locations when the data values are functions. In particular, we propose both
an estimator of the spatial correlation and a functional kriging predictor. We adapt an
optimization criterion used in multivariable spatial prediction in order to estimate the
kriging parameters. The curves are pre-processed by a non-parametric fitting, where
the smoothing parameters are chosen by cross-validation. The approach is illustrated
by analyzing real data based on soil penetration resistances.
CitationGiraldo, R.; Delicado, P.; Mateu, J. Ordinary kriging for function-valued spatial data. "Environmental and ecological statistics", 19 Maig 2010, p. 1-16.
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