A consistent multivariate test of association based on ranks of distances

Abstract

We are concerned with the detection of 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 is consistent against all alternatives. The test has a simple form and is easy to implement. We demonstrate its good power properties in simulations and on examples.

References in corpus (3)

• Measuring and testing dependence by correlation of distances
• Brownian distance covariance
• Introducing the discussion paper by Székely and Rizzo

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