Continuous time-varying kriging for spatial prediction of functional data: An environmental application
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/2167
Tipus de documentArticle
Data publicació2008-07-07
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 2.5 Espanya
Abstract
Spatially correlated functional data is 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 point-wise 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 Fourier basis functions.
The number of basis functions is chosen by cross-validation. Then, the spatial prediction of a curve is obtained as a point-wise 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.
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Giraldo_Delicado_Mateu_2008b.pdf | 534,1Kb | Visualitza/Obre |