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One-sided multivariate tests for high-dimensional datafrom two populations with unknown and unequal covariance matrices |
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| รหัสดีโอไอ | |
| Creator | 1. Samruam Chongcharoen 2. Pawat Paksaranuwat 3. Manad Khamkong |
| Title | One-sided multivariate tests for high-dimensional datafrom two populations with unknown and unequal covariance matrices |
| Publisher | Research and Development Office, Prince of Songkla University |
| Publication Year | 2565 |
| Journal Title | Songklanakarin Journal of Science an Technology (SJST) |
| Journal Vol. | 44 |
| Journal No. | 1 |
| Page no. | 142-148 |
| Keyword | hypothesis testing, twoโ"sample mean vectors, multivariate Behrensโ"fisher problem, high-dimensional data, block diagonal matrix structure |
| URL Website | https://rdo.psu.ac.th/sjst/index.php |
| ISSN | 0125-3395 |
| Abstract | In this paper, we propose a new statistic for testing a one-sided hypothesis of mean vectors from two multivariatenormal populations when the covariance matrices are unknown and unequal for high-dimensional data. As we know that thesample covariance matrix is singular for high-dimensional data, the proposed test is based on the idea of keeping as muchinformation as possible from the sample covariance matrices. The performance of the proposed test is assessed in a simulationstudy with varied situations. The simulation results show that the proposed test was satisfactory in attaining nominal significancevalues close to set levels and the attained test power was excellent in every situation considered. Finally, the efficacy of theproposed test is illustrated with an analysis of DNA microarray data. |
