Finite-sample nonparametric mean tests: Leave-one-out duality and asymptotic optimality

2026-09-04

AI Summary

本文研究了非负随机变量的单边均值假设检验问题，通过开发一种leave-one-out对偶证书框架，提出新的p值方法，并证明其在有限样本下的有效性和最优性。

Abstract

We study finite-sample valid tests of the one-sided mean hypothesis $H_0:μ\leq 1$ against $H_1:μ>1$ for nonnegative random variables. To do so, we develop a leave-one-out dual certificate framework, where certain pointwise inequalities imply p-value validity under the conditional mean null $\mathbb{E}[X_i\mid\mathbf{X}_{-i}]\leq 1$, and which also gives conditions that allow combining dual certificates for p-values to show that their pointwise minimum is also a valid p-value. The framework proves finite-sample validity of Wang and Zhao's nonparametric likelihood-ratio statistic $T_{\mathrm{nplr}}$, yields a new p-value $T_{\mathrm{bin}+}$ extending the Clopper--Pearson binomial test to general nonnegative random variables, and shows that the pointwise minimum $\min\{T_{\mathrm{nplr}},T_{\mathrm{bin}+}\}$ is itself a valid and more powerful p-value. We establish sharp optimality results for such testing problems in two regimes: both $T_{\mathrm{nplr}}$ and $T_{\mathrm{bin}+}$ attain a universal detectability boundary for the null $H_0$ without moment or tail assumptions, and $T_{\mathrm{bin}+}$ attains a nonparametric power lower bound under $n^{-1/2}$-local alternatives to $H_0$. Efficient algorithms and numerical experiments demonstrate substantial finite-sample power gains over existing valid methods.

Problem

• finite-sample
• nonparametric mean tests
• one-sided mean hypothesis
• nonnegative random variables
• p-value validity

Innovation

• leave-one-out dual certificate
• nonparametric mean tests
• p-value validity
• finite-sample optimality
• nonparametric likelihood-ratio statistic