Measuring association and dependence between random vectors

DOI10.1016/j.jmva.2013.08.019zbMath1278.62090OpenAlexW2094741032MaRDI QIDQ391917

Julius Schnieders, Oliver Grothe, Johan Segers

Publication date: 13 January 2014

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2013.08.019

zbMATH Keywords

copulasKendall's tauSpearman's rhoU-statistic

Mathematics Subject Classification ID

Related Items (14)

Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables ⋮ Kendall regression coefficient ⋮ A unified approach to constructing correlation coefficients between random variables ⋮ Copula-based dependence measures for piecewise monotonicity ⋮ Local dependence test between random vectors based on the robust conditional Spearman's \(\rho\) and Kendall's \(\tau\) ⋮ Measuring linear correlation between random vectors ⋮ Vector copulas ⋮ Measuring and testing interdependence among random vectors based on Spearman's \(\rho\) and Kendall's \(\tau\) ⋮ Ordinal pattern dependence as a multivariate dependence measure ⋮ Hierarchical Kendall copulas: Properties and inference ⋮ A framework for measuring association of random vectors via collapsed random variables ⋮ Copula-based dependence measures ⋮ Measuring dependence between random vectors via optimal transport ⋮ О некоторых свойствах симметричной копулы Граббса

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