Jonas Wallin
Universitetslektor, Studierektor för forskarutbildningen, Statistiska institutionen
Multivariate type G Matérn stochastic partial differential equation random fields
Författare
Summary, in English
For many applications with multivariate data, random-field models capturing departures from Gaussianity within realizations are appropriate. For this reason, we formulate a new class of multivariate non-Gaussian models based on systems of stochastic partial differential equations with additive type G noise whose marginal covariance functions are of Matérn type. We consider four increasingly flexible constructions of the noise, where the first two are similar to existing copula-based models. In contrast with these, the last two constructions can model non-Gaussian spatial data without replicates. Computationally efficient methods for likelihood-based parameter estimation and probabilistic prediction are proposed, and the flexibility of the models suggested is illustrated by numerical examples and two statistical applications.
Avdelning/ar
- Statistiska institutionen
Publiceringsår
2020-02
Språk
Engelska
Sidor
215-239
Publikation/Tidskrift/Serie
Journal of the Royal Statistical Society. Series B: Statistical Methodology
Volym
82
Issue
1
Dokumenttyp
Artikel i tidskrift
Förlag
Wiley-Blackwell
Ämne
- Probability Theory and Statistics
Nyckelord
- Matérn covariances
- Multivariate random fields
- Non-Gaussian models
- Spatial statistics
- Stochastic partial differential equations
Status
Published
ISBN/ISSN/Övrigt
- ISSN: 1369-7412