Modeling Spatial and Spatio-Temporal Non Gaussian Processes

Conference paper
First Online:
Part of the Lecture Notes in Statistics book series (LNS, volume 207)

Abstract

The ubiquitous assumption of normality for modeling spatial and spatio-temporal data can be understood for many reasons. A major one is that the multivariate normal distribution is completely characterized by its first two moments. In addition, the stability of multivariate normal distribution under summation and conditioning offers tractability and simplicity. Gaussian spatial processes are well modeled and understood by the statistical and scientific communities, but for a wide range of environmental applications Gaussian spatial or spatio-temporal models cannot reasonably be fitted to the observations.

Keywords

Covariance Function Gaussian Process Multivariate Normal Distribution Gaussian Vector Gaussian Random Process 
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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Notes

Acknowledgements

The author wishes to acknowledge Frédéric Baret, Sébastien Garrigues and Philippe Naveau, co-authors of some of the papers cited here. The research presented in Sect. 7.2.2 was funded by the ANR CLIMATOR project.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Biostatistics and Spatial Processes (BioSP), INRAAvignonFrance

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