Finite Sample Bounds for Composite Hypothesis Testing

2026-08-28

AI Summary

本文研究了有限样本下不对称错误约束的复合二元假设检验问题，利用Rényi散度推导出最优II型错误的可达性和会话界限。

Abstract

We investigate composite binary hypothesis testing in the finite sample regime under asymmetric error constraints. Using Rényi divergences, we derive explicit achievability and converse bounds for the optimal Type II error. When the Type I error is constrained to decay exponentially with sample size, the bounds identify a phase transition and yield a strong converse above it. In the composite problem, the phase transition threshold is given by the joint KL projection over the alternative and null classes. Achievability is obtained through a joint Rényi projection whose log likelihood ratio defines a single test with uniform error control over both hypothesis classes, without requiring the projected pair to be least favourable. For compact convex classes with full support on a finite alphabet, we determine the exact error exponents on both sides of the transition and show that the achievable exponent is attained at a unique Rényi order. The same framework recovers the fixed Type I composite Chernoff--Stein exponent and yields a polynomial refinement of the finite sample achievability result. We further identify conditions under which the projected pair is least favourable at finite sample size.

Problem

• composite hypothesis testing
• finite sample regime
• asymmetric error constraints
• Rényi divergences
• phase transition

Innovation

• Rényi divergences
• composite hypothesis testing
• finite sample regime
• error exponents
• phase transition