Marc Notes:
Originally published: New York: North-Holland, c1983.;Includes bibliographical references (p. [371]-381) and index.

Table of Contents:
1. Vectors
2. Vector Spaces
3. Matrices and Systems of Linear Equations
4. Matrices of Special Type
5. Latent Roots and Latent Vectors
6. Generalized Matrix Inverses
7. Nonnegative and Diagonally Dominant Matrices
References
Index

Publisher Marketing:
This comprehensive text covers both applied and theoretical branches of matrix algebra in the statistical sciences. It also provides a bridge between linear algebra and statistical models. Appropriate for advanced undergraduate and graduate students, the self-contained treatment also constitutes a handy reference for researchers. The only mathematical background necessary is a sound knowledge of high school mathematics and a first course in statistics.
Consisting of two interrelated parts, this volume begins with the basic structure of vectors and vector spaces. The latter part emphasizes the diverse properties of matrices and their associated linear transformations--and how these, in turn, depend upon results derived from linear vector spaces. An overview of introductory concepts leads to more advanced topics such as latent roots and vectors, generalized inverses, and nonnegative matrices. Each chapter concludes with a section on real-world statistical applications, plus exercises that offer concrete examples of the applications of matrix algebra.