Article citationsMore>>
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:
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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.
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