“Generalized measures of correlation for asymmetry, nonlinearity, and beyond”: Some antecedents on causality
Document Type
Journal Article
Publication Title
Journal of the American Statistical Association
ISSN
01621459
Publisher
Taylor and Francis
School
School of Business and Law
RAS ID
35206
Funders
Australian Research Council / Ministry of Science and Technology (MOST), Taiwan
Abstract
© 2020, © 2020 American Statistical Association. This note comments on the generalized measure of correlation (GMC) that was suggested by Zheng, Shi, and Zhang. The GMC concept was partly anticipated in some publications over 100 years earlier by Yule in the Proceedings of the Royal Society, and by Kendall. Other antecedents discussed include work on dependency by Renyi and Doksum and Samarov, together with the Yule–Simpson paradox. The GMC metric partly extends the concept of Granger causality, so that we consider causality, graphical analysis and alternative measures of dependency provided by copulas.
DOI
10.1080/01621459.2020.1768101
Access Rights
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Comments
Allen, D. E., & McAleer, M. (2022). “Generalized measures of correlation for asymmetry, nonlinearity, and beyond”: Some antecedents on causality. Journal of the American Statistical Association, 117(537), 214-224. https://doi.org/10.1080/01621459.2020.1768101