A framework for measuring association of random vectors via collapsed random variables

DOI10.1016/j.jmva.2019.02.012zbMath1432.62324OpenAlexW2916912446WikidataQ128316778 ScholiaQ128316778MaRDI QIDQ2001082

Marius Hofert, Wayne Oldford, Mu Zhu, Avinash Prasad

Publication date: 2 July 2019

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2019.02.012

zbMATH Keywords

hierarchical modelscollapsing functionscollapsed random variablesdependence between random vectorsgraphical test of independenceKendall copulamultivariate Kendall distribution

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Applications of statistics to actuarial sciences and financial mathematics (62P05) Measures of association (correlation, canonical correlation, etc.) (62H20) Characterization and structure theory for multivariate probability distributions; copulas (62H05)

Related Items (3)

Measuring linear correlation between random vectors ⋮ Editorial for the special issue on dependence models ⋮ Measuring dependence between random vectors via optimal transport

Uses Software

• glasso
• qrmtools

Cites Work

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