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The family of multivariate normal distributions with a fixed mean is seen as a Riemannian manifold with Fisher information metric. Two submanifolds naturally arises: one is the submanifold given by the fixed eigenvectors of the covariance matrix; the other is the one given by the fixed eigenvalues. We analyze the geometrical structures of these manifolds such as metric, embedding curvature under e-connection or m-connection. Based on these results, we study (1) the bias of the sample eigenvalues, (2) the asymptotic variance of estimators, (3) the asymptotic information loss caused by neglecting the sample eigenvectors, (4) the derivation of a new estimator that is natural from a geometrical point of view. (C) 2014 Elsevier B.V. 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Inference on the eigenvalues of the covariance matrix of a multivariate normal distribution—Geometrical view
http://hdl.handle.net/10091/18065
http://hdl.handle.net/10091/18065a8d188ca-579c-4f72-913e-df67260fbdc5
名前 / ファイル | ライセンス | アクション |
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Inference_on_the_eigenvalues.pdf (184.6 kB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2015-02-02 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Inference on the eigenvalues of the covariance matrix of a multivariate normal distribution—Geometrical view | |||||
言語 | ||||||
言語 | eng | |||||
DOI | ||||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1016/j.jspi.2014.03.004 | |||||
関連名称 | 10.1016/j.jspi.2014.03.004 | |||||
キーワード | ||||||
主題 | Curved exponential family, Information loss, Fisher information metric, Embedding curvature, Affine connection, Positive definite matrix | |||||
資源タイプ | ||||||
資源 | http://purl.org/coar/resource_type/c_6501 | |||||
タイプ | journal article | |||||
著者 |
Sheena, Yo
× Sheena, Yo |
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出版者 | ||||||
出版者 | ELSEVIER SCIENCE BV | |||||
引用 | ||||||
内容記述タイプ | Other | |||||
内容記述 | JOURNAL OF STATISTICAL PLANNING AND INFERENCE.150:66-83 (2014) | |||||
書誌情報 |
JOURNAL OF STATISTICAL PLANNING AND INFERENCE 巻 150, p. 66-83, 発行日 2014-07 |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | We consider inference on the eigenvalues of the covariance matrix of a multivariate normal distribution. The family of multivariate normal distributions with a fixed mean is seen as a Riemannian manifold with Fisher information metric. Two submanifolds naturally arises: one is the submanifold given by the fixed eigenvectors of the covariance matrix; the other is the one given by the fixed eigenvalues. We analyze the geometrical structures of these manifolds such as metric, embedding curvature under e-connection or m-connection. Based on these results, we study (1) the bias of the sample eigenvalues, (2) the asymptotic variance of estimators, (3) the asymptotic information loss caused by neglecting the sample eigenvectors, (4) the derivation of a new estimator that is natural from a geometrical point of view. | |||||
資源タイプ(コンテンツの種類) | ||||||
内容記述タイプ | Other | |||||
内容記述 | Article | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 0378-3758 | |||||
書誌レコードID | ||||||
収録物識別子タイプ | NCID | |||||
収録物識別子 | AA00253748 | |||||
権利 | ||||||
権利情報 | © 2014 Elsevier B.V. "NOTICE: this is the author’s version of a work that was accepted for publication in JOURNAL OF STATISTICAL PLANNING AND INFERENCE. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in PUBLICATION, [VOL 150, (7, 2014)] DOI:10.1016/j.jspi.2014.03.004" | |||||
出版タイプ | ||||||
出版タイプ | AM | |||||
出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||
WoS | ||||||
表示名 | Web of Science | |||||
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