Modelling skewed spatial random fields through the spatial vine copula
Modelling skewed spatial random fields through the spatial vine copula
复制标题
DOI：
10.1016/j.spasta.2014.01.001
复制
发表时间：
2014-11-01
影响因子：
2.3
通讯作者：
Graeler, Benedikt
中科院分区：
文献类型：
--
作者：
Graeler, Benedikt
研究遵循偏态分布并导致极端行为的现象在许多学科中都很重要。如何描述和建模倾斜空间随机场的相关性仍然是一个具有挑战性的问题。特别是当人们有兴趣从一个空间随机场插值一个样本，展示极端事件，经典的地质统计工具，如克里金依赖于高斯假设无法再现的极端。Copula起源于部分由金融数学驱动的多元极值理论，近年来出现的Copula能够描述高斯领域之外的各种联合尾部行为。在本文中，空间藤Copula的引入，参数化的距离，并允许包括极值行为的空间随机场。新引入的分布拟合到广泛研究的紧急情况和常规情况下的数据集，从空间插值比较2004年（SIC 2004）。所提出的空间藤蔓copula排名前5位的方法，是上级的平均绝对误差方面的所有方法。(C)2014作者由爱思唯尔公司出版
Studying phenomena that follow a skewed distribution and entail an extremal behaviour is important in many disciplines. How to describe and model the dependence of skewed spatial random fields is still a challenging question. Especially when one is interested in interpolating a sample from a spatial random field that exhibits extreme events, classical geostatistical tools like kriging relying on the Gaussian assumption fail in reproducing the extremes. Originating from the multivariate extreme value theory partly driven by financial mathematics, copulas emerged in recent years being capable of describing different kinds of joint tail behaviours beyond the Gaussian realm. In this paper spatial vine copulas are introduced that are parametrized by distance and allow to include extremal behaviour of a spatial random field. The newly introduced distributions are fitted to the widely studied emergency and routine scenario data set from the spatial interpolation comparison 2004 (SIC2004). The presented spatial vine copula ranks within the top 5 approaches and is superior to all approaches in terms of the mean absolute error. (C) 2014 The Author. Published by Elsevier B.V.