Modeling Skewness in Spatial Data Analyis without Data Transformation

  • Philippe Naveau
  • Denis Allard
Part of the Quantitative Geology and Geostatistics book series (QGAG, volume 14)

Skewness is present in a large variety of spatial data sets (rainfalls, winds, etc) but integrating such a skewness still remains a challenge. Classically, the original variables are transformed into a Gaussian vector. Besides the problem of choosing the adequate transform, there are a few difficulties associated with this method. As an alternative, we propose a different way to introduce skewness. The skewness comes from the extension of the multivariate normal distribution to the multivariate skew-normal distribution. This strategy has many advantages. The spatial structure is still captured by the variogram and the classical empirical variogram has a known moment generating function. To illustrate the applicability of such this new approach, we present a variety of simulations.


Covariance Stein Dition Kriging Boulder 


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Copyright information

© Springer 2005

Authors and Affiliations

  • Philippe Naveau
    • 1
  • Denis Allard
    • 2
  1. 1.Dept. of Applied MathematicsUniversity of ColoradoBoulderUSA
  2. 2.INRA, Unité de Biométrie, Domaine St-PaulFrance

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