A hierarchical max-stable spatial model for extreme precipitation

Brian J. Reich, Benjamin A. Shaby

Research output: Contribution to journalArticlepeer-review

108 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1430-1451
Number of pages22
JournalAnnals of Applied Statistics
Issue number4
StatePublished - 2012

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty


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