Robust hypothesis testing and distribution estimation in Hellinger distance
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:2962-2970, 2021.
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.