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.
"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