Original Article |
2015, Vol.37, No.4, pp. 477-484
A new test for the mean vector in high-dimensional data
Knavoot Jiamwattanapong and Samruam Chongcharoen
pp. 477 - 484
Abstract
For the testing of the mean vector where the data are drawn from a multivariate normal population, the renowned Hotelling’s T2 test is no longer valid when the dimension of the data equals or exceeds the sample size. In this study, we consider the problem of testing the hypothesis H :μ 0 and propose a new test based on the idea of keeping more information from the sample covariance matrix. The development of the statistic is based on Hotelling’s T 2 distribution and the new test has invariance property under a group of scalar transformation. The asymptotic distribution is derived under the null hypothesis. The simulation results show that the proposed test performs well and is more powerful when the data dimension increases for a given sample size. An analysis of DNA microarray data with the new test is demonstrated.