R package multivariance: Measuring Multivariate Dependence Using Distance Multivariance. Distance multivariance is a measure of dependence which can be used to detect and quantify dependence. The necessary functions are implemented in this packages, and examples are given. For the theoretic background we refer to the papers: B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using Distance Multivariance. <arXiv:1712.06532>. B. Böttcher, M. Keller-Ressel, R.L. Schilling, Detecting independence of random vectors: generalized distance covariance and Gaussian covariance. VMSTA, 2018, Vol. 5, No. 3, 353-383. <arXiv:1711.07778>. B. Böttcher, M. Keller-Ressel, R.L. Schilling, Distance multivariance: New dependence measures for random vectors. <arXiv:1711.07775>. G. Berschneider, B. Böttcher, On complex Gaussian random fields, Gaussian quadratic forms and sample distance multivariance. <arXiv:1808.07280>.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Böttcher, Björn: Copula versions of distance multivariance and dHSIC via the distributional transform -- a general approach to construct invariant dependence measures (2020)
- Böttcher, Björn; Keller-Ressel, Martin; Schilling, René L.: Detecting independence of random vectors: generalized distance covariance and Gaussian covariance (2018)