A Data-Driven Approach to Robust Hypothesis Testing Using Sinkhorn Uncertainty Sets
A Data-Driven Approach to Robust Hypothesis Testing Using Sinkhorn Uncertainty Sets
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DOI：
10.1109/isit50566.2022.9834367
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Jie Wang;Yao Xie
中科院分区：
文献类型：
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作者：
Jie Wang;Yao Xie
小样本假设检验是一个具有重要实际意义的问题。在本文中，我们研究了稳健的假设检验问题，在数据驱动的方式，我们寻求最坏情况下的检测器分布的不确定性集周围的经验分布的样本使用Sinkhorn距离。与Wasserstein鲁棒性检验相比，该方法在训练样本之外支持相应的最不利分布，从而提供了一种更灵活的检测器。在人工和真实的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力。
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.