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>.
Keywords for this software
References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Quessy, Jean-François: A Szekely-Rizzo inequality for testing general copula homogeneity hypotheses (2021)
- Böttcher, Björn: Copula versions of distance multivariance and dHSIC via the distributional transform -- a general approach to construct invariant dependence measures (2020)
- Edelmann, Dominic; Richards, Donald; Vogel, Daniel: The distance standard deviation (2020)
- Fan, Jianqing; Feng, Yang; Xia, Lucy: A projection-based conditional dependence measure with applications to high-dimensional undirected graphical models (2020)
- Böttcher, Björn; Keller-Ressel, Martin; Schilling, René L.: Distance multivariance: new dependence measures for random vectors (2019)
- Böttcher, Björn; Keller-Ressel, Martin; Schilling, René L.: Detecting independence of random vectors: generalized distance covariance and Gaussian covariance (2018)