A binary reduction yields two directed KL requirements whose sum is a binary Jeffreys divergence, giving sharp prescribed-error minimax rates in three classical testing models.
A coincidence-based test for uniformity given very sparsely sampled discrete data
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Sharp constants in uniformity testing yield an effective Gaussian SNR that ranks rate-optimal tests and produces an explicit maximum-bin formula for calibration assessment.
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High-Confidence Minimax Testing with Prescribed Errors
A binary reduction yields two directed KL requirements whose sum is a binary Jeffreys divergence, giving sharp prescribed-error minimax rates in three classical testing models.