A consistent multivariate test of association based on ranks of distances
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In this paper, the problem of detecting associations between random vectors of any dimension is considered and a powerful test that is applicable in all dimensions and consistent against all alternatives is proposed. But the test has a simple form, is easy to implement, and has good power.Abstract:
SUMMARY We consider the problem of detecting associations between random vectors of any dimension. Few tests of independence exist that are consistent against all dependent alternatives. We propose a powerful test that is applicable in all dimensions and consistent against all alternatives. The test has a simple form, is easy to implement, and has good power.read more
Citations
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Efficient Estimation of Mutual Information for Strongly Dependent Variables
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References
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Journal ArticleDOI
Brownian distance covariance
Gábor J. Székely,Maria L. Rizzo +1 more
TL;DR: The concept of Brownian distance covariance developed by Szekely and Rizzo (2009) was discussed in this article, where two possible extensions of the concept were described.
Proceedings Article
Kernel Measures of Conditional Dependence
TL;DR: A new measure of conditional dependence of random variables, based on normalized cross-covariance operators on reproducing kernel Hilbert spaces, which has a straightforward empirical estimate with good convergence behaviour.
Journal ArticleDOI
Multivariate Nonparametric Tests of Independence
TL;DR: Gieser and Randles, as well as Taskinen, Kankainen, and Oja have introduced and discussed multivariate extensions of the quadrant test of Blomqvist as mentioned in this paper.
Journal ArticleDOI
Introducing the discussion paper by Sz\'{e}kely and Rizzo
TL;DR: Distance covariance as discussed by the authors measures the squared distance covariance between the two variables, i.e., the difference between the pairwise distances between sample values of one variable and the same for the second variable.