Conditional Sampling for Spectrally Discrete Max-Stable Random Fields

Abstract

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.

31 pages. 4 figures. Data analysis removed from the Technical Report (previous version). To appear in Advances in Applied Probability

Cited by in corpus (4)

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• Approximating the conditional density given large observed values via a multivariate extremes framework, with application to environmental data
• Max-stable processes and the functional D-norm revisited
• Discussion of "Statistical Modeling of Spatial Extremes" by A. C. Davison, S. A. Padoan and M. Ribatet