arXiv · 2011.01848
Robust hypothesis testing and distribution estimation in Hellinger distance
Abstract
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.
Explore related subjects
Keep this discovery
Ananda Theertha Suresh. 2020-11-03. Robust hypothesis testing and distribution estimation in Hellinger distance. https://arxiv.org/abs/2011.01848
Cite the original work for its findings. Save a collection to share your selection of sources.