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High-dimensional inference on covariance structures via the extended cross-data-matrix methodology
http://hdl.handle.net/2241/00144265
http://hdl.handle.net/2241/00144265136d2113-bab0-4a28-81e9-cd4992a395fe
名前 / ファイル | ライセンス | アクション |
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JMA_151 (990.1 kB)
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Item type | Journal Article(1) | |||||
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公開日 | 2016-11-02 | |||||
タイトル | ||||||
タイトル | High-dimensional inference on covariance structures via the extended cross-data-matrix methodology | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源 | http://purl.org/coar/resource_type/c_6501 | |||||
タイプ | journal article | |||||
著者 |
Yata, Kazuyoshi
× Yata, Kazuyoshi× Aoshima, Makoto |
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著者別名 |
矢田, 和善
× 矢田, 和善× 青嶋, 誠 |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | 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. | |||||
書誌情報 |
Journal of Multivariate Analysis 巻 151, p. 151-166, 発行日 2016-10 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 0047259X | |||||
書誌レコードID | ||||||
収録物識別子タイプ | NCID | |||||
収録物識別子 | AA0025295X | |||||
DOI | ||||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1016/j.jmva.2016.07.011 | |||||
権利 | ||||||
権利情報 | © 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/ | |||||
著者版フラグ | ||||||
値 | author | |||||
出版者 | ||||||
出版者 | Elsevier |