Copula versions of distance multivariance and dHSIC via the distributional transform – a general approach to construct invariant dependence measures
DOI10.1080/02331888.2020.1748029zbMath1440.62224arXiv1912.01388OpenAlexW3014114608MaRDI QIDQ5110808
Publication date: 23 May 2020
Published in: Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1912.01388
dHSICdistributional transformcopula-based measuresdistance multivariancemultivariate independence tests
Hypothesis testing in multivariate analysis (62H15) Measures of association (correlation, canonical correlation, etc.) (62H20) Characterization and structure theory for multivariate probability distributions; copulas (62H05)
Uses Software
Cites Work
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- Measuring and testing dependence by correlation of distances
- On the estimation of Spearman's rho and related tests of independence for possibly discontinuous multivariate data
- On the distributional transform, Sklar's theorem, and the empirical copula process
- On rank correlation measures for non-continuous random variables
- On nonparametric measures of dependence for random variables
- Some new copula based distribution-free tests of independence among several random variables
- Distance multivariance: new dependence measures for random vectors
- Detecting independence of random vectors: generalized distance covariance and Gaussian covariance
- Kernel-Based Tests for Joint Independence
- Distance Metrics for Measuring Joint Dependence with Application to Causal Inference
- Algorithmic Learning Theory
- Testing for independence in arbitrary distributions
- Discussion of: Brownian distance covariance
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