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Nonparametric measure of multivariate dependence between several random variables proposed in Gaißer, Ruppert & Schmid (2010). Unlike Pearson's or rank correlation (Kendall's tau, Spearman's rho), it picks up dependence of any form.
Usage:
> multphi2(data)
Where:
data - n x d matrix containing n realizations of d random variables, association between which is to be measures.
Output: phi - 1x1 measure of association (phi = 0 corresponds to mutual independence of variables in columns of data, phi = 1 - increasing deterministic (not necessarily linear) relationship.
References:
Gaißer, S., Ruppert, M., & Schmid, F. (2010). A multivariate version of Hoeffding’s Phi-Square. Journal of Multivariate Analysis, 101(10), 2571-2586.
Cite As
Ivan Medovikov (2026). Multivariate Hoeffding's Phi-Squared (https://ch.mathworks.com/matlabcentral/fileexchange/46773-multivariate-hoeffding-s-phi-squared), MATLAB Central File Exchange. Retrieved .
Categories
Find more on Probability Distributions and Hypothesis Tests in Help Center and MATLAB Answers
General Information
- Version 1.0.0.0 (1.79 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.0.0 |
