High-Confidence Minimax Testing with Prescribed Errors

Classical minimax lower bounds for testing are typically derived for fixed error probabilities, while high-confidence results often impose a common failure probability.

We study prescribed-error testing, in which the level and the target type II error may be small and of different orders.

Standard prior-based reductions generally aggregate the two errors into a single quantity and therefore do not capture their distinct roles.

We develop a general lower-bound technique based on a binary reduction that preserves the separate roles of the two error targets.

The reduction yields two directed Kullback-Leibler information requirements, corresponding respectively to the level and the target type II error.

When both directed mixture divergences can be controlled, they combine into a binary Jeffreys divergence, leading to the logarithmic dependence on the level and the target type II error.

Applying the framework to Gaussian sequence testing, multinomial uniformity testing, and continuous uniformity testing over Hölder balls, we obtain lower bounds that match corresponding high-confidence upper bounds and hence establish prescribed-error minimax rates sharp up to constant factors.