Matrix Algebra Theory, Computations, and Applications in Statistics

ISBN-10: 0387708723
ISBN-13: 9780387708720
Edition: 2007
Authors: James E. Gentle
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Description: Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. The first part of this book presents the relevant aspects of the theory of matrix algebra for applications in statistics. This part begins  More...

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Book details

Copyright year: 2007
Publisher: Springer
Publication date: 7/27/2007
Binding: Hardcover
Pages: 530
Size: 6.25" wide x 9.25" long x 1.10" tall
Weight: 1.936
Language: English

Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. The first part of this book presents the relevant aspects of the theory of matrix algebra for applications in statistics. This part begins with the fundamental concepts of vectors and vector spaces, next covers the basic algebraic properties of matrices, then describes the analytic properties of vectors and matrices in the multivariate calculus, and finally discusses operations on matrices in solutions of linear systems and in eigenanalysis. This part is essentially self-contained. The second part of the book begins with a consideration of various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices. The second part also describes some of the many applications of matrix theory in statistics, including linear models, multivariate analysis, and stochastic processes. The brief coverage in this part illustrates the matrix theory developed in the first part of the book. The first two parts of the book can be used as the text for a course in matrix algebra for statistics students, or as a supplementary text for various courses in linear models or multivariate statistics. The third part of this book covers numerical linear algebra. It begins with a discussion of the basics of numerical computations, and then describes accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Although the book is not tied to any particular software system, it describesand gives examples of the use of modern computer software for numerical linear algebra. This part is essentially self-contained, although it assumes some ability to program in Fortran or C and/or the ability to use R/S-Plus or Matlab. This part of the book can be used as the text for a course in statistical computing, or as a supplementary text for various courses that emphasize computations. The book includes a large number of exercises with some solutions provided in an appendix.

Basic vector/matrix structure and notation
Vectors and vector spaces
Basic properties of matrices
Vector/matrix derivatives and integrals
Matrix transformations and factorizations
Solution of linear systems
Evaluation of eigenvalues and eigenvectors
Special matrices and operations useful in modeling and data analysis
Selected applications in statistics
Numerical methods
Numerical linear algebra
Software for numerical linear algebra
Notation and definitions
Solutions and hints for selected exercises

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