Pair-copula constructions of multiple dependence
Pair-copula constructions of multiple dependence
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DOI：
10.1016/j.insmatheco.2007.02.001
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发表时间：
2009-04-01
影响因子：
1.9
通讯作者：
Bakken, Henrik
中科院分区：
文献类型：
--
作者：
Aas, Kjersti;Czado, Claudia;Bakken, Henrik
在贝德福德、库克和乔工作的基础上，我们展示了如何使用级联对联结对多变量数据进行建模，这些数据在尾部表现出复杂的依赖模式，一次作用于两个变量。我们使用一般多元分布的对联结分解，并提出一种执行推理的方法。模型构建本质上是分层的，各个级别对应于条件集中更多变量的合并，使用对系词作为简单的构建块。对联结分解模型也代表了构建高维联结的一种非常灵活的方法。我们将该方法应用于财务数据集。我们的方法代表了开发艺术无监督算法的第一步，该算法探索可能的配对联结模型的空间，也可以自动应用于巨大的数据集。 (C) 2007 Elsevier B.V. 保留所有权利。
Building on the work of Bedford, Cooke and Joe, we show how multivariate data, which exhibit complex patterns of dependence in the tails, can be modelled using a cascade of pair-copulae, acting on two variables at a time. We use the pair-copula decomposition of a general multivariate distribution and propose a method for performing inference. The model construction is hierarchical in nature, the various levels corresponding to the incorporation of more variables in the conditioning sets, using pair-copulae as simple building blocks. Pair-copula decomposed models also represent a very flexible way to construct higher-dimensional copulae. We apply the methodology to a financial data set. Our approach represents the first step towards the development of art unsupervised algorithm that explores the space of possible pair-copula models, that also can be applied to huge data sets automatically. (C) 2007 Elsevier B.V. All rights reserved.