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November 2017 A two-sample test for high-dimension, low-sample-size data under the strongly spiked eigenvalue model
Aki Ishii
Hiroshima Math. J. 47(3): 273-288 (November 2017). DOI: 10.32917/hmj/1509674448

Abstract

A common feature of high-dimensional data is that the data dimension is high, however, the sample size is relatively low. We call such data HDLSS data. In this paper, we consider a new two-sample test for high-dimensional data under the strongly spiked eigenvalue (SSE) model. We consider the distance-based two-sample test under the SSE model. We introduce the noise-reduction (NR) methodology and apply that to the two-sample test. Finally, we give simulation studies and demonstrate the new test procedure by using microarray data sets.

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Aki Ishii. "A two-sample test for high-dimension, low-sample-size data under the strongly spiked eigenvalue model." Hiroshima Math. J. 47 (3) 273 - 288, November 2017. https://doi.org/10.32917/hmj/1509674448

Information

Received: 27 April 2016; Revised: 5 October 2016; Published: November 2017
First available in Project Euclid: 3 November 2017

zbMATH: 06836007
MathSciNet: MR3719445
Digital Object Identifier: 10.32917/hmj/1509674448

Subjects:
Primary: 62H15
Secondary: 34L20

Rights: Copyright © 2017 Hiroshima University, Mathematics Program

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Vol.47 • No. 3 • November 2017
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