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Spatial Matern Fields Driven by Non-Gaussian Noise
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
2014 (English)In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 41, no 3, 557-579 p.Article in journal (Refereed) Published
Abstract [en]

The article studies non-Gaussian extensions of a recently discovered link between certain Gaussian random fields, expressed as solutions to stochastic partial differential equations (SPDEs), and Gaussian Markov random fields. The focus is on non-Gaussian random fields with Matern covariance functions, and in particular, we show how the SPDE formulation of a Laplace moving-average model can be used to obtain an efficient simulation method as well as an accurate parameter estimation technique for the model. This should be seen as a demonstration of how these techniques can be used, and generalizations to more general SPDEs are readily available.

Place, publisher, year, edition, pages
2014. Vol. 41, no 3, 557-579 p.
Keyword [en]
expectation-maximization algorithm, Laplace noise, Markov random fields, Matern covariances, non-Gaussian, stochastic partial differential equation
National Category
Probability Theory and Statistics
URN: urn:nbn:se:umu:diva-93219DOI: 10.1111/sjos.12046ISI: 000340493800001OAI: diva2:774328
Available from: 2014-12-22 Created: 2014-09-15 Last updated: 2014-12-22Bibliographically approved

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Bolin, David
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