A HIERARCHICAL MAX-STABLE SPATIAL MODEL FOR EXTREME PRECIPITATION

• 成果类型: Article
• 署名作者: Reich, Brian J.; Shaby, Benjamin A.
• 署名单位: North Carolina State University; University of California System; University of California Berkeley
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/12-AOAS591
• 发表日期: 2012
• 页码: 1430-1451
• 关键词: bayesian-inference; likelihood

摘要

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.