Hoeffding's relative entropy threshold test between empirical distributions is asymptotically optimal for both one- and two-sample hypothesis testing, with a strong converse for the two-sample case.
Robust kullback-leibler divergence and universal hypothesis testing for continuous distributions
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Asymptotically Optimal Tests for One- and Two-Sample Problems
Hoeffding's relative entropy threshold test between empirical distributions is asymptotically optimal for both one- and two-sample hypothesis testing, with a strong converse for the two-sample case.