Difference: Papers20100623105844 (1 vs. 2)

Revision 22010-07-23 - TWikiAdminUser

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META TOPICPARENT name="GeostatisticsPapers"
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META TOPICPARENT name="AI_GEOSTATSPapers"
 Title: Analysis of Rainfall Data by Robust Spatial Statistics using S+ SPATIALSTATS

Date:

Authors: Marc G. Genton & Reinhard Furrer

Link: fileadmin/Documents/SIC97_GIDA/Genton_2.pdf

Abstract:

REFERENCE

Journal of Geographic Information and Decision Analysis, Vol. 2, No. 2, pp. 116-126, 1998

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.

-- TWikiAdminUser - 2010-06-16

Revision 12010-06-23 - TWikiAdminUser

 
META TOPICPARENT name="GeostatisticsPapers"
Title: Analysis of Rainfall Data by Robust Spatial Statistics using S+ SPATIALSTATS

Date:

Authors: Marc G. Genton & Reinhard Furrer

Link: fileadmin/Documents/SIC97_GIDA/Genton_2.pdf

Abstract:

REFERENCE

Journal of Geographic Information and Decision Analysis, Vol. 2, No. 2, pp. 116-126, 1998

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.

-- TWikiAdminUser - 2010-06-16

 
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