论文标题
电子价值分类器两样本测试
E-Valuating Classifier Two-Sample Tests
论文作者
论文摘要
我们为基于电子价值的高维数据引入了强大的深层分类器两样本测试，称为电子价值分类器两样本测试（E-C2ST）。我们的测试结合了现有工作中关于分裂似然比测试和预测独立性测试的想法。所得的电子值适用于任何时间 - 录音顺序两样本测试。此功能允许在构建测试统计数据中更有效地利用数据。通过模拟和实际数据应用程序，我们从经验上证明，E-C2ST通过将数据集划分为标准分类器两样本测试的常规两级（培训和测试）方法来实现增强的统计能力。该策略增加了测试的功能，同时保持I型误差远低于所需的显着性水平。
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.
