论文标题
强大的假设检验和分布估计在Hellinger距离
Robust hypothesis testing and distribution estimation in Hellinger distance
论文作者
论文摘要
我们提出了一个简单的鲁棒假设检验，该假设具有与最佳Neyman-Pearson测试的样本复杂性，但对常数却具有鲁棒性，可在Hellinger距离下进行分布扰动。我们讨论了这种可靠测试在估算Hellinger距离分布的适用性。我们从经验上证明了对规范分布的测试的力量。
We propose a simple robust hypothesis test that has the same sample complexity as that of the optimal Neyman-Pearson test up to constants, but robust to distribution perturbations under Hellinger distance. We discuss the applicability of such a robust test for estimating distributions in Hellinger distance. We empirically demonstrate the power of the test on canonical distributions.
