Fast and exact simulation of univariate and bivariate Gaussian random fields. Issue 1 (19th June 2018)

• Record Type: Journal Article
• Authors: Moreva, Olga; Schlather, Martin
• Is Part Of: Stat. Volume 7:Issue 1(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-06-19
• Subjects: circulant embedding -- compactly supported covariance function -- cut‐off embedding -- matrix‐valued covariance function -- multivariate geostatistics; Statistics -- Periodicals; 519.2
• DOI: 10.1002/sta4.188
• Languages: English
• ISSNs: 2049-1573

Abstract

Abstract : Circulant embedding is a powerful algorithm for fast simulation of stationary Gaussian random fields on a rectangular grid in R n, which works perfectly for compactly supported covariance functions. Cut‐off circulant embedding techniques have been developed for univariate random fields for dimensions up to R 3 and rely on the modification of a covariance function outside the simulation window, such that the modified covariance function is compactly supported. In this paper, we propose extensions of the cut‐off approach for univariate and bivariate Gaussian random fields. In particular, we provide a method for simulating bivariate fields with a powered exponential model and the Matérn model for certain sets of parameters. Copyright © 2018 John Wiley & Sons, Ltd.