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Conditional sampling for spectrally discrete max-stable random fields

Published online by Cambridge University Press:  01 July 2016

Yizao Wang*
Affiliation:
University of Michigan
Stilian A. Stoev*
Affiliation:
University of Michigan
*
Postal address: Department of Statistics, University of Michigan, 439 West Hall, 1085 South University, Ann Arbor, MI 48109-1107, USA.
Postal address: Department of Statistics, University of Michigan, 439 West Hall, 1085 South University, Ann Arbor, MI 48109-1107, USA.
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Abstract

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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.

Type
General Applied Probability
Copyright
Copyright © Applied Probability Trust 2011 

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