Article citationsMore>>

Lopes, M., Jacob, L. and Wainright, M.J. (2011) A More Powerful Two-Sample Test in High Dimensions Using Random Projection. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F. and Weinberger, K.Q., Eds., Advances in Neural Information Processing Systems, Vol. 24, Curran Associates Inc., Red Hook, 1206-1214.

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.