2024-03-28T15:03:12Z
https://tsukuba.repo.nii.ac.jp/oai
oai:tsukuba.repo.nii.ac.jp:00039600
2022-04-27T09:09:47Z
117:1697
117:786
3:62:5592:626
High-dimensional inference on covariance structures via the extended cross-data-matrix methodology
矢田, 和善
青嶋, 誠
Yata, Kazuyoshi
Aoshima, Makoto
Tests of the correlation matrix between two subsets of a high-dimensional random vector are considered. The test statistic is based on the extended cross-data-matrix methodology (ECDM) and shown to be unbiased. The ECDM estimator is also proved to be consistent and asymptotically Normal in high-dimensional settings. The authors propose a test procedure based on the ECDM estimator and evaluate its size and power, both theoretically and numerically. They give several applications of the ECDM estimator and illustrate the performance of the test procedure using microarray data.
journal article
Elsevier
2016-10
application/pdf
Journal of Multivariate Analysis
151
151
166
0047259X
AA0025295X
https://tsukuba.repo.nii.ac.jp/record/39600/files/JMA_151.pdf
eng
10.1016/j.jmva.2016.07.011
© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/