Conditional sampling for spectrally discrete max-stable random fields

By Yizao Wang, Stilian A. Stoev. Advances in Applied Probability, 2010

91 citations recorded by the source corpus.

Max-stable random fields play a central role in modeling extreme value phenomena. We obtain an explicit formula for the conditional probability in general max-linear models, which include a large class of max-stable random fields. As a consequence, we develop an algorithm for efficient and exact sampling from the conditional distributions. Our method provides a computational solution to the prediction problem for spectrally discrete max-stable random fields. This work offers new tools and a new perspective to many statistical inference problems for spatial extremes, arising, for example, in meteorology, geology, and environmental applications.

Subfields: Environmental Engineering, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c3f1e5381430a5b353d3cbc9c8aac828cd22e777

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Citations in the corpus

An M‐estimator of spatial tail dependence (2014, background): “Moreover, they are likelihood-based and therefore cannot be used to ﬁt, e.g., spectrally discrete max-stable models (Wang and Stoev, 2011).”
Conditional simulation of max-stable processes (2012, methodology): “Wang & Stoev (2011) call the partitionθ the hitting scenario.”
Spatial extremes: Max-stable processes at work (2013, background): “In Wang and Stoev (2011), a first solution was proposed for max-linear Journal de la Société Française de Statistique, Vol. 154 No. 2 156-177 http://www.sfds.asso.fr/journal © Société Française de Statistique et Société Mathématique de France (2013) ISSN: 2102-6238 processes but in this paper we…”