论文标题
使用Sinkhorn不确定性集的数据驱动的方法来实现强大的假设检验
A Data-Driven Approach to Robust Hypothesis Testing Using Sinkhorn Uncertainty Sets
论文作者
论文摘要
小样本场景的假设测试实际上是一个重要的问题。在本文中，我们以数据驱动的方式研究了可靠的假设检验问题，在此过程中，我们在使用sindhorn距离的围绕样品中的经验分布的分布不确定性集中寻求最坏的检测器。与Wasserstein强大的测试相比，在训练样本之外，支持相应的最小有利分布，这提供了更灵活的检测器。在合成数据集和实际数据集上进行了各种数值实验，以验证我们提出的方法的竞争性能。
Hypothesis testing for small-sample scenarios is a practically important problem. In this paper, we investigate the robust hypothesis testing problem in a data-driven manner, where we seek the worst-case detector over distributional uncertainty sets centered around the empirical distribution from samples using Sinkhorn distance. Compared with the Wasserstein robust test, the corresponding least favorable distributions are supported beyond the training samples, which provides a more flexible detector. Various numerical experiments are conducted on both synthetic and real datasets to validate the competitive performances of our proposed method.
