Abstract
The aim of this work is to introduce a new local linear estimate for spatial stationary process with known mean and a modelized covariance structure. This new estimation is based on a multivariate statistical method, developed in Tenenhaus(1998) and applied to spatial data by Elkettani (2001). This method based on the maximization of the covariance function gives meaningful weights and leads to an easy algorithm for the selection of a neighbourhood configuration simplifying the stepwise selection algorithm used for spatial data by Haslett (1989).
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References
Elkettani(2001): Analyse des redondances et régression PLS appliquées aux données spatiales. Comparaison avec l’estimation par krigeage et par inverse de la distance. To be published in: la Revue de Statistique Appliquée.
Haslett(1989): Geostatistical neighbourhoods and subset selection; M. Armstrong (ed), Geostatistics; 2: 569–577
Isaaks et Srivastava(1989): An introduction to applied geostatistics; Oxford University Press.
Tenenhaus (1998), M: La régression PLS, théorie et pratique; éditions TECHNIP.
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© 2001 Springer Science+Business Media Dordrecht
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Elkettani, Y. (2001). The PLS Regression in Spatial Statistical Estimation. In: Monestiez, P., Allard, D., Froidevaux, R. (eds) geoENV III — Geostatistics for Environmental Applications. Quantitative Geology and Geostatistics, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0810-5_51
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DOI: https://doi.org/10.1007/978-94-010-0810-5_51
Publisher Name: Springer, Dordrecht
Print ISBN: 978-0-7923-7107-6
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