Multivariate Multiple Comparison Procedures among Mean Vectors in High-dimenional Settings

Abstract

In this paper, we discuss multivariate multiple comparison among mean vectors in high-dimensional settings. In particular, in the case of pairwise comparisons, we propose procedures that construct the approximate simultaneous confidence intervals. We review the results of asymptotic expansions for the upper 100α percentiles of our proposed statistic. Also, we derive the asymptotic null distribution for this statistic under non-normality. Finally, numerical results by Monte Carlo simulations are given.