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Robust hypothesis testing and distribution estimation in Hellinger distance
November 03, 2020 Β· Declared Dead Β· π International Conference on Artificial Intelligence and Statistics
"No code URL or promise found in abstract"
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Authors
Ananda Theertha Suresh
arXiv ID
2011.01848
Category
math.ST
Cross-listed
cs.IT,
cs.LG,
stat.ML
Citations
8
Venue
International Conference on Artificial Intelligence and Statistics
Last Checked
6 months ago
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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