Nonparametric two-sample tests of high dimensional mean vectors via random integration.

ABSTRACT: Testing the equality of the means in two samples is a fundamental statistical inferential problem. Most of the existing methods are based on the sum-of-squares or supremum statistics. They are possibly powerful in some situations, but not in others, and they do not work in a unified way. Using random integration of the difference, we develop a framework that includes and extends many existing methods, especially in high-dimensional settings, without restricting the same covariance matrices or sparsity. Under a general multivariate model, we can derive the asymptotic properties of the proposed test statistic without specifying a relationship between the data dimension and sample size explicitly. Specifically, the new framework allows us to better understand the test's properties and select

SUBMITTER: Jiang Y 

PROVIDER: S-EPMC11028772 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature