Statistical Modeling of Spatial Extremes

被引：396
作者：
Davison, A. C. [1 ]
Padoan, S. A. [2 ]
Ribatet, M. [3 ]
机构：
[1] Ecole Polytech Fed Lausanne, EPFL FSB IMA STAT, Inst Math, Stn 8,Chair Stat, CH-1015 Lausanne, Switzerland
[2] Univ Padua, Dept Stat Sci, I-35121 Padua, Italy
[3] Univ Montpellier 2, UMR CNRS 5149, I3M, F-34095 Montpellier 5, France
来源：
STATISTICAL SCIENCE | 2012年 / 27卷 / 02期
基金：
瑞士国家科学基金会;
关键词：
Annual maximum analysis; Bayesian hierarchical model; Brown-Resnick process; composite likelihood; copula; environmental data analysis; Gaussian process; generalized extreme-value distribution; geostatistics; latent variable; max-stable process; statistics of extremes; LIKELIHOOD INFERENCE; MULTIVARIATE; DEPENDENCE; TREND;
D O I：
10.1214/11-STS376
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
The areal modeling of the extremes of a natural process such as rainfall or temperature is important in environmental statistics; for example, understanding extreme areal rainfall is crucial in flood protection. This article reviews recent progress in the statistical modeling of spatial extremes, starting with sketches of the necessary elements of extreme value statistics and geostatistics. The main types of statistical models thus far proposed, based on latent variables, on copulas and on spatial max-stable processes, are described and then are compared by application to a data set on rainfall in Switzerland. Whereas latent variable modeling allows a better fit to marginal distributions, it fits the joint distributions of extremes poorly, so appropriately-chosen copula or max-stable models seem essential for successful spatial modeling of extremes.
引用
收藏
页码：161 / 186
页数：26
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