Distance correlation is a measure of statistical dependence between two paired random vectors of arbitrary, and possibly different, dimension. It was introduced in 2005 by Gabor J. Szekely to address a deficiency of Pearson's correlation, which can be zero even for variables that are actually dependent. The population distance correlation is zero if and only if the two random vectors are independent, so unlike ordinary correlation, a distance correlation of zero does imply independence, allowing it to detect nonlinear as well as linear relationships.
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In statistics and in probability theory, distance correlation is a measure of dependence between two paired random vectors of arbitrary, not necessarily equal, dimension.
In Branch: Probability and Statistics, Lead sentence
In statistics and in probability theory, distance correlation is a measure of dependence between two paired random vectors of arbi
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