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Matrix algebra = theory, computations and applications in statistics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Matrix algebra/ by James E. Gentle.
Reminder of title:	theory, computations and applications in statistics /
Author:	Gentle, James E.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xxxii, 693 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Matrices. -
Online resource:	https://doi.org/10.1007/978-3-031-42144-0
ISBN:	9783031421440

Matrix algebra = theory, computations and applications in statistics /
Gentle, James E.

Matrix algebratheory, computations and applications in statistics /[electronic resource] :by James E. Gentle. - Third edition. - Cham :Springer International Publishing :2024. - xxxii, 693 p. :ill., digital ;24 cm. - Springer texts in statistics,2197-4136. - Springer texts in statistics..

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.

This book presents the theory of matrix algebra for statistical applications, explores various types of matrices encountered in statistics, and covers numerical linear algebra. Matrix algebra is one of the most important areas of mathematics in data science and in statistical theory, and previous editions had essential updates and comprehensive coverage on critical topics in mathematics. This 3rd edition offers a self-contained description of relevant aspects of matrix algebra for applications in statistics. It begins with fundamental concepts of vectors and vector spaces; covers basic algebraic properties of matrices and analytic properties of vectors and matrices in multivariate calculus; and concludes with a discussion on operations on matrices, in solutions of linear systems and in eigenanalysis. It also includes discussions of the R software package, with numerous examples and exercises. Matrix Algebra considers various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes special properties of those matrices; as well as describing various applications of matrix theory in statistics, including linear models, multivariate analysis, and stochastic processes. It begins with a discussion of the basics of numerical computations and goes on to describe accurate and efficient algorithms for factoring matrices, how to solve linear systems of equations, and the extraction of eigenvalues and eigenvectors. It covers numerical linear algebra-one of the most important subjects in the field of statistical computing. The content includes greater emphases on R, and extensive coverage of statistical linear models. Matrix Algebra is ideal for graduate and advanced undergraduate students, or as a supplementary text for courses in linear models or multivariate statistics. It's also ideal for use in a course in statistical computing, or as a supplementary text for various courses that emphasize computations.

ISBN: 9783031421440

Standard No.: 10.1007/978-3-031-42144-0doiSubjects--Topical Terms:

516894
Matrices.

LC Class. No.: QA188

Dewey Class. No.: 512.9434

Matrix algebra = theory, computations and applications in statistics /
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245 1 0 $a Matrix algebra $h [electronic resource] : $b theory, computations and applications in statistics / $c by James E. Gentle.
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300 $a xxxii, 693 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer texts in statistics, $x 2197-4136
505 0 $a 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.
520 $a This book presents the theory of matrix algebra for statistical applications, explores various types of matrices encountered in statistics, and covers numerical linear algebra. Matrix algebra is one of the most important areas of mathematics in data science and in statistical theory, and previous editions had essential updates and comprehensive coverage on critical topics in mathematics. This 3rd edition offers a self-contained description of relevant aspects of matrix algebra for applications in statistics. It begins with fundamental concepts of vectors and vector spaces; covers basic algebraic properties of matrices and analytic properties of vectors and matrices in multivariate calculus; and concludes with a discussion on operations on matrices, in solutions of linear systems and in eigenanalysis. It also includes discussions of the R software package, with numerous examples and exercises. Matrix Algebra considers various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes special properties of those matrices; as well as describing various applications of matrix theory in statistics, including linear models, multivariate analysis, and stochastic processes. It begins with a discussion of the basics of numerical computations and goes on to describe accurate and efficient algorithms for factoring matrices, how to solve linear systems of equations, and the extraction of eigenvalues and eigenvectors. It covers numerical linear algebra-one of the most important subjects in the field of statistical computing. The content includes greater emphases on R, and extensive coverage of statistical linear models. Matrix Algebra is ideal for graduate and advanced undergraduate students, or as a supplementary text for courses in linear models or multivariate statistics. It's also ideal for use in a course in statistical computing, or as a supplementary text for various courses that emphasize computations.
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