Numerical Linear Algebra for Applications in Statistics (Statistics and Computing) - Hardcover

Gentle, James E.

Hardcover

ISBN 10: 0387985425 ISBN 13: 9780387985428

Publisher: Springer, 1998

Synopsis

Accurate and efficient computer algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Regardless of the software system used, the book describes and gives examples of the use of modern computer software for numerical linear algebra. It begins with a discussion of the basics of numerical computations, and then describes the relevant properties of matrix inverses, factorisations, matrix and vector norms, and other topics in linear algebra. The book is essentially self- contained, with the topics addressed constituting the essential material for an introductory course in statistical computing. Numerous exercises allow the text to be used for a first course in statistical computing or as supplementary text for various courses that emphasise computations.

About this title

Review

From a review:

JOURNAL OF AMERICAN STATISTICAL ASSOCIATION

"Gentle brings to this book (as well as his other recent books on further aspects of statistical computing) his vast knowledge and experience in the mathematics of scientific computing, the practical aspects of software development, and teaching. The presentation is exceptionally clear and well-sign-boarded. ...The writing style, though very precise, conveys a warmth and enthusiasm that will appeal to students."

 

Synopsis

Numerical linear algebra is one of the most important subjects in the field of statistical computing. Statistical methods in many areas of application require computations with vectors and matrices. This book describes accurate and efficient computer 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 describes and gives examples of the use of modern computer software for numerical linear algebra. An understanding of numerical linear algebra requires basic knowledge both of linear algebra and of how numerical data are stored and manipulated in the computer. The book begins with a discussion of the basics of numerical computations, and then describes the relevant properties of matrix inverses, matrix factorizations, matrix and vector norms, and other topics in linear algebra; hence, the book is essentially self- contained. The topics addressed in this book constitute the most important material for an introductory course in statistical computing, and should be covered in every such course.

The book includes exercises and can be used as a text for a first course in statistical computing or as supplementary text for various courses that emphasize computations. James Gentle is University Professor of Computational Statistics at George Mason University. During a thirteen-year hiatus from academic work before joining George Mason, he was director of research and design at the world's largest independent producer of Fortran and C general-purpose scientific software libraries. These libraries implement many algorithms for numerical linear algebra. He is a Fellow of the American Statistical Association and member of the International Statistical Institute.

Edition details

• Publisher: Springer
• Publication date: 1998
• ISBN 10: 0387985425
• ISBN 13: 9780387985428
• Binding: Hardcover
• Language: English
• Number of pages: 234