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.
A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations // Annals of Mathematical Statistics
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.ST 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.