Robust hypothesis testing and distribution estimation in Hellinger distance

Ananda Theertha Suresh

Keywords: [ Learning Theory and Statistics ] [ Robust Statistics and Machine Learning ]

[ Abstract ]
Wed 14 Apr 12:45 p.m. PDT — 2:45 p.m. PDT


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.

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