Ordinal pattern dependence as a multivariate dependence measure

[摘要] In this article, we show that the recently introduced ordinal pattern dependence fits into the axiomatic framework of general multivariate dependence measures, i.e., measures of dependence between two multivariate random objects. Furthermore, we consider multivariate generalizations of established univariate dependence measures like Kendall's tau, Spearman's rho and Pearson's correlation coefficient. Among these, only multivariate Kendall's tau proves to take the dynamical dependence of random vectors stemming from multidimensional time series into account. Consequently, the article focuses on a comparison of ordinal pattern dependence and multivariate Kendall's tau in this context. To this end, limit theorems for multivariate Kendall's tau are established under the assumption of near-epoch dependent data-generating time series. We analyze how ordinal pattern dependence compares to multivariate Kendall's tau and Pearson's correlation coefficient on theoretical grounds. Additionally, a simulation study illustrates differences in the kind of dependencies that are revealed by multivariate Kendall's tau and ordinal pattern dependence. (C) 2021 Elsevier Inc. All rights reserved.

[发布日期] 2021-11-01

[关键词] Concordance ordering;Limit theorems;Multivariate dependence;Ordinal pattern;Ordinal pattern dependence;Time series