A consistent multivariate test of association based on ranks of distances
About
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.
Ruth Heller, Yair Heller, Malka Gorfine• 2012
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Dependence Detection | Linear dependence model | Power72.7 | 25 | |
| Dependence Testing | Laplace A | Power60.6 | 22 | |
| Dependence Testing | Tree ring A | Power91.8 | 3 | |
| Dependence Detection | Triangle dependence model | Power97 | 2 | |
| Dependence Detection | Crescent dependence model | Power99.2 | 2 | |
| Dependence Detection | Circles dependence model | Power99.5 | 2 | |
| Dependence Testing | Variance B | Power98.8 | 2 |
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