A Two-Sample Test for the Mean Vector in High-Dimensional Data


ศ.ดร.สำรวม จงเจริญ, นายKnavoot Jiamwattanapong


Applied Science and Innovative Research


Modern measurement technology has enabled the capture of high-dimensional data by researchers and
statisticians and classical statistical inferences, such as the renowned Hotelling’s T2
test, are no longer
valid when the dimension of the data equals or exceeds the sample size. Importantly, when correlations
among variables in a dataset exist, taking them into account in the analysis method would provide more
accurate conclusions. In this article, we consider the hypothesis testing problem for two mean vectors
in high-dimensional data with an underlying normality assumption. A new test is proposed based on the
idea of keeping more information from the sample covariances. The asymptotic null distribution of the
test statistic is derived. The simulation results show that the proposed test performs well comparing
with other competing tests and becomes more powerful when the dimension increases for a given
sample size. The proposed test is also illustrated with an analysis of DNA microarray data

(2017). A Two-Sample Test for the Mean Vector in High-Dimensional Data. Applied Science and Innovative Research, 1(2), 118-130.