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 structures.
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Deep-testing: the case of dependence detection
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 structures.