Continuous Time-Varying Kriging for Spatial Prediction of Functional Data: An Environmental Application
By Ramón Giraldo, Pedro Delicado, Jorge Mateu. 2010
99 citations recorded by the source corpus.
Spatially correlated functional data are present in a wide range of environmental disciplines and, in this context, efficient prediction of curves is a key issue. We present an approach for spatial prediction based on the functional linear pointwise model adapted to the case of spatially correlated curves. First, a smoothing process is applied to the curves by expanding the curves and the functional parameters in terms of a set of basis functions. The number of basis functions is chosen by cross-validation. Then, the spatial prediction of a curve is obtained as a pointwise linear combination of the smoothed data. The prediction problem is solved by estimating a linear model of coregionalization to set the spatial dependence among the fitted coefficients. We extend an optimization criterion used in multivariable geostatistics to the functional context. The method is illustrated by smoothing and predicting temperature curves measured at 35 Canadian weather stations.
Subfields: Environmental Engineering, Genetics, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/80673ef14e9b48b24006c835e045e9bfa21db9c0
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In Environmental Engineering
In Genetics
In Nature and Landscape Conservation
Citations in the corpus
- A Universal Kriging predictor for spatially dependent functional data of a Hilbert Space (2013, methodology): “…on this topic: indeed, theoretical results in this direction have been recently derived (Giraldo et al. (2008b, 2011); Delicado et al. (2010); Giraldo et al. (2010a); Monestiez and Ner-ini (2008); Nerini et al. (2010)) moving from the pioneering work by Goulard and Voltz (1993), but this…”