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| > > | Date: 1 May 1998 | |||||||
| Authors: Marc G. Genton & Reinhard Furrer | ||||||||
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| < < | Link: fileadmin/Documents/SIC97_GIDA/Genton_2.pdf | |||||||
| > > | Link: Genton_2.pdf | |||||||
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| > > | This paper discusses the use of robust geostatistical methods on a data set of rainfall measurements in Switzerland. The variables are detrended via non-parametric estimation penalized with a smoothing parameter. The optimal trend is computed with a smoothing parameter based on cross-validation. Then, the variogram is estimated by a highly robust estimator of scale. The parametric variogram model is fitted by generalized least squares, thus taking account of the variance-covariance structure of the variogram estimates. Comparison of kriging with the initial measurements is completed and yields interesting results. All these computations are done with the software S+SPATIALSTATS, extended with new functions in S+ that are made available. | |||||||
| REFERENCE Journal of Geographic Information and Decision Analysis, Vol. 2, No. 2, pp. 116-126, 1998 | ||||||||
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| < < | Abstract This paper discusses the use of robust geostatistical methods on a data set of rainfall measurements in Switzerland. The variables are detrended via non-parametric estimation penalized with a smoothing parameter. The optimal trend is computed with a smoothing parameter based on cross-validation. Then, the variogram is estimated by a highly robust estimator of scale. The parametric variogram model is fitted by generalized least squares, thus taking account of the variance-covariance structure of the variogram estimates. Comparison of kriging with the initial measurements is completed and yields interesting results. All these computations are done with the software S+SPATIALSTATS, extended with new functions in S+ that are made available. | |||||||
| Keywords: Robustness; Trend; Variogram; Generalized least squares; Kriging. | ||||||||
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