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Redundancy analysis and PLS regression applied to spatial data. Comparison with kriging estimation and by inverse distance. (Analyse des redondances et régression PLS appliquées aux données spatiales. Comparaison avec l’estimation par krigeage et par inverse de la distance.) (French) Zbl 0983.62067
Summary: The aim of this article is the extension of redundancy analysis and partial least squares regression, PLS, particularly adapted for the explanation of data tables with multicollinearities, to spatial data. This application, based on the covariance structure of spatial data, gives for redundancy analysis exactly the same results as the simple kriging which is the optimal estimation, while the PLS regression gives coefficients more meaningful and then useful for the choice of the more explicative observations for the point to estimate. Then the PLS regression is an interesting tool for spatial data analysis, completing the kriging, and is more adapted than the inverse distance method which does not use the covariance structure.
MSC:
62M30 Inference from spatial processes
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
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