Modeling Skewness in Spatial Data Analyis without Data Transformation
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.
KeywordsSpatial Data Moment Generate Function Multivariate Normal Distribution Stochastic Frontier Analysis Gaussian Vector
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