Description
Book SynopsisMatrices.- Submatrices and Partitioned Matrices.- Linear Dependence and Independence.- Linear Spaces: Row and Column Spaces.- Trace of a (Square) Matrix.- Geometrical Considerations.- Linear Systems: Consistency and Compatibility.- Inverse Matrices.- Generalized Inverses.- Idempotent Matrices.- Linear Systems: Solutions.- Projections and Projection Matrices.- Determinants.- Linear, Bilinear, and Quadratic Forms.- Matrix Differentiation.- Kronecker Products and the Vec and Vech Operators.- Intersections and Sums of Subspaces.- Sums (and Differences) of Matrices.- Minimization of a Second-Degree Polynomial (in n Variables) Subject to Linear Constraints.- The Moore-Penrose Inverse.- Eigenvalues and Eigenvectors.- Linear Transformations.- Erratum.
Trade ReviewFrom a review:
THE AUSTRALIAN AND NEW ZEALAND JOURNAL OF STATISTICS
"This is a book that will be welcomed by many statisticians at most stages of professional development. ...It is essentially a carefully sequenced and tightly interlocking collections of proofs in an elementary, though very pure mathematical style."
Table of ContentsPreface. - Matrices. - Submatrices and partitioned matricies. - Linear dependence and independence. - Linear spaces: row and column spaces. - Trace of a (square) matrix. - Geometrical considerations. - Linear systems: consistency and compatability. - Inverse matrices. - Generalized inverses. - Indepotent matrices. - Linear systems: solutions. - Projections and projection matrices. - Determinants. - Linear, bilinear, and quadratic forms. - Matrix differentiation. - Kronecker products and the vec and vech operators. - Intersections and sums of subspaces. - Sums (and differences) of matrices. - Minimzation of a second-degree polynomial (in n variables) subject to linear constraints. - The Moore-Penrose inverse. - Eigenvalues and Eigenvectors. - Linear transformations. - References. - Index.
