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Burczynski, M.E., Peterson, R.L., Twine, N.C., Zuberek, K.A., Brodeur, B.J., Casciotti, L., Maganti, V., Reddy, P.S., Strahs, A., Immermann, F., Spinelli, W., Schwertschlag, U., Slager, A.M., Cotreau, M.M. and Dorner, A.J. (2006) Molecular Classification of Crohn’s Disease and Ulcerative Colitis Patients Using Transcriptional Profiles in Peripheral Blood Mononuclear Cells. The Journal of Molecular Diagnostics, 8, 51-61.
https://doi.org/10.2353/jmoldx.2006.050079

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.