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Matrix algebra : theory, computations and applications in statistics
- 作者： Gentle, James E., author.
- 其他作者：
- 其他題名：
- Springer texts in statistics.
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 版本：Second edition.
- 叢書名： Springer texts in statistics,
- 主題： Matrices. , Statistics. , Statistical Theory and Methods. , Algebra. , Statistics and Computing/Statistics Programs. , Probability and Statistics in Computer Science. , Computational Mathematics and Numerical Analysis. , Numeric Computing.
- ISBN： 9783319648675 (electronic bk.) 、 9783319648668 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: Part I Linear Algebra -- 1 Basic Vector/Matrix Structure and Notation -- 2 Vectors and Vector Spaces -- 3 Basic Properties of Matrices -- 4 Vector/Matrix Derivatives and Integrals -- 5 Matrix Transformations and Factorizations -- 6 Solution of Linear Systems -- 7 Evaluation of Eigenvalues and Eigenvectors -- Part II Applications in Data Analysis -- 8 Special Matrices and Operations Useful in Modeling andData Analysis -- 9 Selected Applications in Statistics -- Part III Numerical Methods and Software -- 10 Numerical Methods -- 11 Numerical Linear Algebra -- 12 Software for Numerical Linear Algebra -- Appendices and Back Matter -- Bibliography -- Index.
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讀者標籤：
- 系統號： 005410553 | 機讀編目格式
館藏資訊
Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices. Finally, it covers numerical linear algebra, beginning with a discussion of the basics of numerical computations, and following up with accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors.
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