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Geostatistical Modelling Using Non-Gaussian Matern Fields

Jonas Wallin (Institutionen för matematiska vetenskaper, matematisk statistik) ; David Bolin (Institutionen för matematiska vetenskaper, matematisk statistik)
Scandinavian Journal of Statistics (0303-6898). Vol. 42 (2015), 3, p. 872-890.
[Artikel, refereegranskad vetenskaplig]

This work provides a class of non-Gaussian spatial Matern 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.

Nyckelord: Laplace, Markov random fields, Matern covariances, MCEM algorithm, normal inverse Gaussian, SPDE, variance Gamma

Denna post skapades 2015-09-15.
CPL Pubid: 222462


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Institutioner (Chalmers)

Institutionen för matematiska vetenskaper, matematisk statistik (2005-2016)


Sannolikhetsteori och statistik

Chalmers infrastruktur