He, Y., Zhang, M., Zhang, X. and Zhou, W. (2020) High-Dimensional Two-Sample Mean Vectors Test and Support Recovery with Factor Adjustment. Computational Statistics and Data Analysis, 151, Article ID: 107004.
https://doi.org/10.1016/j.csda.2020.107004

has been cited by the following article:

• TITLE: Dimension Reduction for Detecting a Difference in Two High-Dimensional Mean Vectors

  AUTHORS: Whitney V. Worley, Dean M. Young, Phil D. Young

  KEYWORDS: Homoscedastic Covariance Matrices, Test Power, Monte Carlo Simulation, Moore-Penrose Inverse, Singular Value Decomposition

  JOURNAL NAME: Open Journal of Statistics, Vol.11 No.1, February 26, 2021

  ABSTRACT: We consider the efficacy of a proposed linear-dimension-reduction method to potentially increase the powers of five hypothesis tests for the difference of two high-dimensional multivariate-normal population-mean vectors with the assumption of homoscedastic covariance matrices. We use Monte Carlo simulations to contrast the empirical powers of the five high-dimensional tests by using both the original data and dimension-reduced data. From the Monte Carlo simulations, we conclude that a test by Thulin [1], when performed with post-dimension-reduced data, yielded the best omnibus power for detecting a difference between two high-dimensional population-mean vectors. We also illustrate the utility of our dimension-reduction method real data consisting of genetic sequences of two groups of patients with Crohn’s disease and ulcerative colitis.