Matrix algebra

H. Yanai; H. Ishii

doi:10.1016/B978-0-08-044894-7.01341-5

Matrix algebra

H. Yanai
, H. Ishii

医学部・内科学（第４）

研究成果: 書籍/レポートタイプへの寄稿 › 章

2 被引用数 (Scopus)

抄録

Definition and basic operations of matrices are described. Fundamental properties of matrices such as rank, determinants, inverse, and positive (semi)definiteness are also identified. Eigenvalues and eigenvectors, spectral and singular value decompositions are described with numerical examples. Projection matrices, generalized inverse of a matrix, Kronecker product, and Vec operator are briefly explained. Basic applications of matrix algebra to multivariate analysis, a correlation matrix, and principal-component analyses are provided.

本文言語	英語
ホスト出版物のタイトル	International Encyclopedia of Education
出版社	Elsevier Ltd
ページ	253-259
ページ数	7
ISBN（印刷版）	9780080448947
DOI	https://doi.org/10.1016/B978-0-08-044894-7.01341-5
出版ステータス	出版済み - 2010

All Science Journal Classification (ASJC) codes

経済学、計量経済学および金融学一般

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10.1016/B978-0-08-044894-7.01341-5

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引用スタイル

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abstract = "Definition and basic operations of matrices are described. Fundamental properties of matrices such as rank, determinants, inverse, and positive (semi)definiteness are also identified. Eigenvalues and eigenvectors, spectral and singular value decompositions are described with numerical examples. Projection matrices, generalized inverse of a matrix, Kronecker product, and Vec operator are briefly explained. Basic applications of matrix algebra to multivariate analysis, a correlation matrix, and principal-component analyses are provided.",

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AU - Ishii, H.

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AB - Definition and basic operations of matrices are described. Fundamental properties of matrices such as rank, determinants, inverse, and positive (semi)definiteness are also identified. Eigenvalues and eigenvectors, spectral and singular value decompositions are described with numerical examples. Projection matrices, generalized inverse of a matrix, Kronecker product, and Vec operator are briefly explained. Basic applications of matrix algebra to multivariate analysis, a correlation matrix, and principal-component analyses are provided.

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Matrix algebra

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All Science Journal Classification (ASJC) codes

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