Every f-divergence yields a constraint on the achievable error region of a binary test, the hockey-stick family makes these constraints exactly tight, and any Neyman-Pearson boundary can be realized by a specially constructed distribution pair.
Empirically Estimable Classification Bounds Based on a Nonparametric Divergence Measure // IEEE Transactions on Signal Processing
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Bounding Neyman-Pearson Region with $f$-Divergences
Every f-divergence yields a constraint on the achievable error region of a binary test, the hockey-stick family makes these constraints exactly tight, and any Neyman-Pearson boundary can be realized by a specially constructed distribution pair.