Numerical Linear Algebra for Applications in Statistics

Numerical linear algebra is one of the most important subjects in the field ofstatistical computing. Statistical methods in many areas of application requirecom... » 

Numerical linear algebra is one of the most important subjects in the field ofstatistical computing. Statistical methods in many areas of application requirecomputations with vectors and matrices. This book describes accurate andefficient computer algorithms for factoring matrices solving linear systems ofequations and extracting eigenvalues and eigenvectors. Although the book isnot tied to any particular software system it describes and gives examples ofthe use of modern computer software for numerical linear algebra. Anunderstanding of numerical linear algebra requires basic knowledge both oflinear algebra and of how numerical data are stored and manipulated in thecomputer. The book begins with a discussion of the basics of numericalcomputations and then describes the relevant properties of matrix inversesmatrix factorizations matrix and vector norms and other topics in linearalgebra hence the book is essentially self contained. The topics addressedin this book constitute the most important material for an introductory coursein statistical computing and should be covered in every such course. The bookincludes exercises and can be used as a text for a first course in statisticalcomputing or as supplementary text for various courses that emphasizecomputations. James Gentle is University Professor of Computational Statisticsat George Mason University. During a thirteenyear hiatus from academic workbefore joining George Mason he was director of research and design at theworlds largest independent producer of Fortran and C generalpurposescientific software libraries. These libraries implement many algorithms fornumerical linear algebra. He is a Fellow of the American StatisticalAssociation and member of the International Statistical Institute. He has heldseveral national «