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Asymptotic Normality for Inference on Multisample, High-Dimensional Mean Vectors Under Mild Conditions
http://hdl.handle.net/2241/00124791
http://hdl.handle.net/2241/001247916cf3bf3c-8480-4417-86e9-1ceac936d7b4
名前 / ファイル | ライセンス | アクション |
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MCAP_17-2 (994.7 kB)
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Item type | Journal Article(1) | |||||
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公開日 | 2015-06-16 | |||||
タイトル | ||||||
タイトル | Asymptotic Normality for Inference on Multisample, High-Dimensional Mean Vectors Under Mild Conditions | |||||
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 |
Aoshima, Makoto
× Aoshima, Makoto× Yata, Kazuyoshi |
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著者別名 |
青嶋, 誠
× 青嶋, 誠× 矢田, 和善 |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | In this paper, we consider the asymptotic normality for various inference problems on multisample and high-dimensional mean vectors. We verify that the asymptotic normality of concerned statistics is proved under mild conditions for high-dimensional data. We show that the asymptotic normality can be justified theoretically and numerically even for non-Gaussian data. We introduce the extended cross-data-matrix (ECDM) methodology to construct an unbiased estimator at a reasonable computational cost. With the help of the asymptotic normality, we show that the concerned statistics given by ECDM can ensure consistency properties for inference on multisample and high-dimensional mean vectors. We give several applications such as confidence regions for high-dimensional mean vectors, confidence intervals for the squared norm and the test of multisample mean vectors. We also provide sample size determination so as to satisfy prespecified accuracy on inference. Finally, we give several examples by using a microarray data set. | |||||
書誌情報 |
Methodology and computing in applied probability 巻 17, 号 2, p. 419-439, 発行日 2015-06 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1387-5841 | |||||
DOI | ||||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1007/s11009-013-9370-7 | |||||
権利 | ||||||
権利情報 | © Springer Science+Business Media New York 2013. The final publication is available at link.springer.com. | |||||
著者版フラグ | ||||||
値 | author | |||||
出版者 | ||||||
出版者 | Springer US |