Numerical Linear Algebra for Applications in Statistics by James E. Gentle
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, factorizations, 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 the first course in statistical computing or as a supplementary text for various courses that emphasize computations.
James E. Gentle
Number Of Pages
Statistics and Computing
Number of Volumes
Scholarly & Professional
LC Classification Number
J. Chambers, S. Sheather, W. Eddy, Wolfgang Härdle
Table Of Content
Computer Storage and Manipulation of Data.- Basic Vector/Matrix Computations.- Solution of Linear Systems.- Computation of Eigenvectors and Eigenvalues and the Singular Value Decomposition.- Software for Numerical Linear Algebra.- Applications in Statistics.
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."