E-Valuating Classifier Two-Sample Tests

• URL: http://arxiv.org/abs/2210.13027v2
• Date: Tue, 30 Apr 2024 10:02:06 GMT
• Title: E-Valuating Classifier Two-Sample Tests
• Authors: Teodora Pandeva, Tim Bakker, Christian A. Naesseth, Patrick Forré,
• Abstract summary: Our test combines ideas from existing work-valid split likelihood ratio tests and predictive independence tests. The resulting E-values are suitable for anytime sequential two-sample tests.
• Score: 11.248868528186332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a powerful deep classifier two-sample test for high-dimensional data based on E-values, called E-value Classifier Two-Sample Test (E-C2ST). Our test combines ideas from existing work on split likelihood ratio tests and predictive independence tests. The resulting E-values are suitable for anytime-valid sequential two-sample tests. This feature allows for more effective use of data in constructing test statistics. Through simulations and real data applications, we empirically demonstrate that E-C2ST achieves enhanced statistical power by partitioning datasets into multiple batches beyond the conventional two-split (training and testing) approach of standard classifier two-sample tests. This strategy increases the power of the test while keeping the type I error well below the desired significance level.

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