Linear Algebra and Linear Models

• Vector Spaces and Subspaces.- Rank, Inner Product and Nonsingularity.- Eigenvalues and Positive Definite Matrices.- Generalized Inverses.- Inequalities for Eigenvalues and Singular Values.- Rank Additivity and Matrix Partial Orders.- Linear Estimation.- Tests of Linear Hypotheses.- Linear Mixed Models.- Miscellaneous Topics.- Additional Exercises on Rank.

From the book reviews: "The author presents basic ideas and concepts of linear algebra and linear models equally from both theoretical and applications perspectives. ... This book is well presented and structured for an upper level undergraduate course for students of statistics. Further, it can serve as a textbook for an entry-level graduate course. Not only that, it can act as a reference book for practitioners and research statisticians." (Technometrics, Vol. 55 (1), August, 2012)