An approximate randomization test for high-dimensional two-sample Behrens-Fisher problem under arbitrary covariances

TL;DR

This paper introduces an approximate randomization test for high-dimensional two-sample Behrens-Fisher problems that remains valid under arbitrary covariances and unbalanced sample sizes, with proven asymptotic correctness and good power.

Contribution

It proposes a novel randomization test method that adapts to all asymptotic distributions of the test statistic without restrictive covariance assumptions.

Findings

The test maintains correct asymptotic level across various conditions.

It exhibits strong power properties in high-dimensional settings.

Numerical experiments show superior performance compared to existing methods.

Abstract

This paper is concerned with the problem of comparing the population means of two groups of independent observations. An approximate randomization test procedure based on the test statistic of Chen and Qin (2010) is proposed. The asymptotic behavior of the test statistic as well as the randomized statistic is studied under weak conditions. In our theoretical framework, observations are not assumed to be identically distributed even within groups. No condition on the eigenstructure of the covariance matrices is imposed. And the sample sizes of the two groups are allowed to be unbalanced. Under general conditions, all possible asymptotic distributions of the test statistic are obtained. We derive the asymptotic level and local power of the approximate randomization test procedure. Our theoretical results show that the proposed test procedure can adapt to all possible asymptotic distributions of the test statistic and always has correct test level asymptotically. Also, the proposed test procedure has good power behavior. Our numerical experiments show that the proposed test procedure has favorable performance compared with several alternative test procedures.