Testing the equality of multivariate means when $p>n$ by combining the Hoteling and Simes tests

TL;DR

This paper introduces a new high-dimensional testing method combining Hotelling and Simes tests to compare mean vectors, effective when the number of variables exceeds sample size, and robust to Gaussian assumption violations.

Contribution

The paper presents a novel combined testing procedure for high-dimensional mean comparison that is valid asymptotically and adaptable to unequal covariance matrices.

Findings

Proposed test outperforms existing methods in simulations

Method remains valid under dependency and non-Gaussian conditions

Robust to violations of Gaussian assumptions

Abstract

We propose a method of testing the shift between mean vectors of two multivariate Gaussian random variables in a high-dimensional setting incorporating the possible dependency and allowing $p > n$. This method is a combination of two well-known tests: the Hotelling test and the Simes test. The tests are integrated by sampling several dimensions at each iteration, testing each using the Hotelling test, and combining their results using the Simes test. We prove that this procedure is valid asymptotically. This procedure can be extended to handle non-equal covariance matrices by plugging in the appropriate extension of the Hotelling test. Using a simulation study, we show that the proposed test is advantageous over state-of-the-art tests in many scenarios and robust to violation of the Gaussian assumption.