Asymptotics of higher criticism via Gaussian approximation

TL;DR

This paper develops a Gaussian approximation framework to analyze the asymptotic distribution of higher criticism statistics under dependent $t$-statistics, extending the understanding beyond independent Gaussian assumptions.

Contribution

It introduces a unified approach to derive the asymptotic distribution of higher criticism under dependence, using a new Gaussian approximation theorem for empirical processes.

Findings

Asymptotic distributions are derived for dependent $t$-statistics.

The framework extends higher criticism analysis to dependent data.

Explicit asymptotic distributions are obtained under finite moment conditions.

Abstract

Higher criticism is a large-scale testing procedure that can attain the optimal detection boundary for sparse and faint signals. However, there has been a lack of knowledge in most existing works about its asymptotic distribution for more realistic settings other than the independent Gaussian assumption while maintaining the power performance as much as possible. In this paper, we develop a unified framework to analyze the asymptotic distributions of the higher criticism statistic and the more general multi-level thresholding statistic when the individual test statistics are dependent $t$-statistics under a finite ($2+\delta$)-th moment condition, $0<\delta\leq1$. The key idea is to approximate the global test statistic by the supremum of an empirical process indexed by a normalized class of indicator or thresholding functions, respectively. A new Gaussian approximation theorem for suprema of empirical processes with dependent observations is established to derive the explicit asymptotic distributions.