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A universal kriging approach for spatial functional data
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DOI：10.1007/s00477-013-0691-4.png)
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In a wide range of scientific fields the outputs coming from certain measurements often come in form of curves. In this paper we give a solution to the problem of spatial prediction of non-stationary functional data. We propose a new predictor by extending the classical universal kriging predictor for univariate data to the context of functional data. Using an approach similar to that used in univariate geostatistics we obtain a matrix system for estimating the weights of each functional variable on the prediction. The proposed methodology is validated by analyzing a real dataset corresponding to temperature curves obtained in several weather stations of Canada.
Keyword:
Cross-validation
Functional random variable
Smoothed curves
Trace-variogram
Universal kriging
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3.6
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