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Robust hypothesis testing and distribution estimation in Hellinger distance (2011.01848v1)
Published 3 Nov 2020 in math.ST, cs.IT, cs.LG, math.IT, stat.ML, and stat.TH
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.
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