Geostatistical Modelling Using Non-Gaussian Matérn Fields

Jonas Wallin, David Bolin

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


This work provides a class of non-Gaussian spatial Matérn fields which are useful for analysing geostatistical data. The models are constructed as solutions to stochastic partial differential equations driven by generalized hyperbolic noise and are incorporated in a standard geostatistical setting with irregularly spaced observations, measurement errors and covariates. A maximum likelihood estimation technique based on the Monte Carlo expectation-maximization algorithm is presented, and a Monte Carlo method for spatial prediction is derived. Finally, an application to precipitation data is presented, and the performance of the non-Gaussian models is compared with standard Gaussian and transformed Gaussian models through cross-validation.
Original languageEnglish (US)
Pages (from-to)872-890
Number of pages19
JournalScandinavian Journal of Statistics
Issue number3
StatePublished - Jan 1 2015
Externally publishedYes

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Generated from Scopus record by KAUST IRTS on 2020-05-04


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