The multinomial minimax risk for uniformity testing against $l_p$ alternatives converges exactly to $2Phi(-u^*/2)$ in the intermediate regime, proven via a conditional central limit theorem.
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2 Pith papers cite this work. Polarity classification is still indexing.
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PRADAS derives a Bayes-optimal mirror statistic for any splitting scheme, establishes asymptotic FDR control under weak dependence, and optimizes the split ratio as a stopping time to improve power over standard equal-split methods.
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Sharp Lower Bound on the Minimax Risk for Multinomial Uniformity Testing via a Conditional Central Limit Theorem
The multinomial minimax risk for uniformity testing against $l_p$ alternatives converges exactly to $2Phi(-u^*/2)$ in the intermediate regime, proven via a conditional central limit theorem.
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PRADAS: PRior-Assisted DAta Splitting for False Discovery Rate Control
PRADAS derives a Bayes-optimal mirror statistic for any splitting scheme, establishes asymptotic FDR control under weak dependence, and optimizes the split ratio as a stopping time to improve power over standard equal-split methods.