Non-parametric tests for cross-dependence based on multivariate extensions of ordinal patterns
Publication date
2025-04-10
Document type
Forschungsartikel
Author
Organisational unit
Scopus ID
Publisher
Elsevier
Series or journal
Computational Statistics and Data Analysis
ISSN
Periodical volume
210
Article ID
108189
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Cross-dependence
Entropy
Multivariate ordinal patterns
Ordinal pattern dependence
Spatial ordinal patterns
Abstract
Analyzing the cross-dependence within sequentially observed pairs of random variables is an interesting mathematical problem that also has several practical applications. Most of the time, classical dependence measures like Pearson's correlation are used to this end. This quantity, however, only measures linear dependence and has other drawbacks as well. Different concepts for measuring cross-dependence in sequentially observed random vectors, which are based on so-called ordinal patterns or multivariate generalizations of them, are described. In all cases, limiting distributions of the corresponding test statistics are derived. In a simulation study, the performance of these statistics is compared with three competitors, namely, classical Pearson's and Spearman's correlation as well as the rank-based Chatterjee's correlation coefficient. The applicability of the test statistics is illustrated by using them on two real-world data examples.
Description
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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Published version
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