pith. sign in

arxiv: 1201.3522 · v3 · pith:VQVU4D5Gnew · submitted 2012-01-17 · 📊 stat.ME

A consistent multivariate test of association based on ranks of distances

Ruth Heller , Yair Heller , Malka Gorfine This is my paper
classification 📊 stat.ME
keywords consistenttestalternativesapplicableassociationassociationsconcerneddemonstrate
0
0 comments X
read the original 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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Deep-testing: the case of dependence detection

    stat.ML 2026-04 unverdicted novelty 6.0

    Deep neural networks trained to classify simulated samples under null and alternative hypotheses produce a test statistic that outperforms nineteen competing methods for independence testing across varied dependence s...