SPATIAL MODELING OF EXTREME SNOW DEPTH

被引：118

作者：

Blanchet, Juliette ^{[1
]}

Davison, Anthony C. ^{[1
]}

机构：

[1] Ecole Polytech Fed Lausanne, EPFL FSB MATHAA STAT, CH-1015 Lausanne, Switzerland

来源：

ANNALS OF APPLIED STATISTICS | 2011年 / 5卷 / 03期

关键词：

Climate space; extremal coefficient; extreme value theory; Max-stable process; pairwise likelihood; snow depth data; MULTIVARIATE EXTREMES; LIKELIHOOD INFERENCE; HIERARCHICAL MODEL; VALUE DEPENDENCE;

D O I：

10.1214/11-AOAS464

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 spatial modeling of extreme snow is important for adequate risk management in Alpine and high altitude countries. A natural approach to such modeling is through the theory of max-stable processes, an infinite-dimensional extension of multivariate extreme value theory. In this paper we describe the application of such processes in modeling the spatial dependence of extreme snow depth in Switzerland, based on data for the winters 1966-2008 at 101 stations. The models we propose rely on a climate transformation that allows us to account for the presence of climate regions and for directional effects, resulting from synoptic weather patterns. Estimation is performed through pairwise likelihood inference and the models are compared using penalized likelihood criteria. The max-stable models provide a much better fit to the joint behavior of the extremes than do independence or full dependence models.

引用

页码：1699 / 1725

页数：27