Nonparametric estimation of the tree structure of a nested Archimedean copula

作者：Segers Johan; Uyttendaele Nathan^*

来源：Computational Statistics & Data Analysis, 2014, 72: 190-204.

DOI：10.1016/j.csda.2013.10.028

• Archimedean copula
• Dependence
• Nested Archimedean copula
• Hierarchical Archimedean copula
• Rooted tree
• Subtree
• Kendall distribution
• Fan
• Triple
• Nonparametric inference

One of the features inherent in nested Archimedean copulas, also called hierarchical Archimedean copulas, is their rooted tree structure. A nonparametric, rank-based method to estimate this structure is presented. The idea is to represent the target structure as a set of trivariate structures, each of which can be estimated individually with ease. Indeed, for any three variables there are only four possible rooted tree structures and, based on a sample, a choice can be made by performing comparisons between the three bivariate margins of the empirical distribution of the three variables. The set of estimated trivariate structures can then be used to build an estimate of the target structure. The advantage of this estimation method is that it does not require any parametric assumptions concerning the generator functions at the nodes of the tree.

• 出版日期2014-4