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.


Spatial Data Moment Generate Function Multivariate Normal Distribution Stochastic Frontier Analysis Gaussian Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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