© 1998

Numerical Linear Algebra for Applications in Statistics


Part of the Statistics and Computing book series (SCO)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. James E. Gentle
    Pages 1-45
  3. James E. Gentle
    Pages 47-86
  4. James E. Gentle
    Pages 87-121
  5. James E. Gentle
    Pages 137-159
  6. James E. Gentle
    Pages 161-182
  7. Back Matter
    Pages 183-221

About this book


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. He has held several national


Analysis Eigenvalue Eigenvector Fitting Matrix algebra algorithms best fit computer linear algebra statistics

Authors and affiliations

  1. 1.Institute for Computational Sciences and InformaticsGeorge Mason UniversityFairfaxUSA

Bibliographic information


From a review:


"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."