---
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.'
address:
- |
    Department of Statistics\
    North Carolina State University\
    Raleigh, North Carolina 27560\
    USA\
- |
    Department of Statistics\
    University of California—Berkeley\
    Berkeley, Berkeley 94720\
    USA\
author:
- 
- 
title: 'A hierarchical max-stable spatial model for extreme precipitation'
---

Introduction {#sintro}
============

Spatial statistical techniques are crucial for accurately quantifying the likelihood of extreme events and monitoring changes in their frequency and intensity. Extreme events are by definition rare, therefore, estimation of local climate characteristics 