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Efficient simulation of Brown-Resnick processes based on variance reduction of Gaussian processes

Oesting, Marco and Strokorb, Kirstin 2018. Efficient simulation of Brown-Resnick processes based on variance reduction of Gaussian processes. Advances in Applied Probability 50 (4) , pp. 1155-1175. 10.1017/apr.2018.54

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Brown-Resnick processes are max-stable processes that are associated to Gaussian processes. Their simulation is often based on the corresponding spectral representation which is not unique. We study to what extent simulation accuracy and efficiency can be improved by minimizing the maximal variance of the underlying Gaussian process. Such a minimization is a difficult mathematical problem that also depends on the geometry of the simulation domain. We extend Matheron’s (1974) seminal contribution in two aspects: (i) making his description of a minimal maximal variance explicit for convex variograms on symmetric domains and (ii) proving that the same strategy reduces the maximal variance also for a huge class of non-convex variograms representable through a Bernstein function. A simulation study confirms that our non-costly modification can lead to substantial improvements among Gaussian representations. We also compare it with three other established algorithms.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Subjects: Q Science > QA Mathematics
Publisher: Applied Probability Trust
ISSN: 0001-8678
Date of First Compliant Deposit: 13 November 2018
Date of Acceptance: 17 October 2018
Last Modified: 28 Jun 2019 04:51

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