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December 2012 A hierarchical max-stable spatial model for extreme precipitation
Brian J. Reich, Benjamin A. Shaby
Ann. Appl. Stat. 6(4): 1430-1451 (December 2012). DOI: 10.1214/12-AOAS591

Abstract

Extreme environmental phenomena such as major precipitation events manifestly exhibit spatial dependence. Max-stable processes are a class of asymptotically-justified models that are capable of representing spatial dependence among extreme values. While these models satisfy modeling requirements, they are limited in their utility because their corresponding joint likelihoods are unknown for more than a trivial number of spatial locations, preventing, in particular, Bayesian analyses. In this paper, we propose a new random effects model to account for spatial dependence. We show that our specification of the random effect distribution leads to a max-stable process that has the popular Gaussian extreme value process (GEVP) as a limiting case. The proposed model is used to analyze the yearly maximum precipitation from a regional climate model.

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Brian J. Reich. Benjamin A. Shaby. "A hierarchical max-stable spatial model for extreme precipitation." Ann. Appl. Stat. 6 (4) 1430 - 1451, December 2012. https://doi.org/10.1214/12-AOAS591

Information

Published: December 2012
First available in Project Euclid: 27 December 2012

zbMATH: 1257.62120
MathSciNet: MR3058670
Digital Object Identifier: 10.1214/12-AOAS591

Keywords: Gaussian extreme value process , Generalized extreme value distribution , positive stable distribution , regional climate model

Rights: Copyright © 2012 Institute of Mathematical Statistics

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Vol.6 • No. 4 • December 2012
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