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- 580 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
Linear Algebra and Matrix Analysis for Statistics
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About This Book
Assuming no prior knowledge of linear algebra, this self-contained text offers a gradual exposition to linear algebra without sacrificing the rigor of the subject. It presents both the vector space approach and the canonical forms in matrix theory. The book covers important topics in linear algebra that are useful for statisticians, including the concept of rank, the fundamental theorem of linear algebra, projectors, and quadratic forms. It also provides an extensive collection of exercises on theoretical concepts and numerical computations.
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Yes, you can access Linear Algebra and Matrix Analysis for Statistics by Sudipto Banerjee, Anindya Roy in PDF and/or ePUB format, as well as other popular books in Mathematics & Algebra. We have over one million books available in our catalogue for you to explore.
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Table of contents
- Cover
- Half Title
- Series Page
- Title Page
- Copyright Page
- Dedication
- Contents
- Preface
- 1. Matrices, Vectors and Their Operations
- 2. Systems of Linear Equations
- 3. More on Linear Equations
- 4. Euclidean Spaces
- 5. The Rank of a Matrix
- 6. Complementary Subspaces
- 7. Orthogonality, Orthogonal Subspaces and Projections
- 8. More on Orthogonality
- 9. Revisiting Linear Equations
- 10. Determinants
- 11. Eigenvalues and Eigenvectors
- 12. Singular Value and Jordan Decompositions
- 13. Quadratic Forms
- 14. The Kronecker Product and Related Operations
- 15. Linear Iterative Systems, Norms and Convergence
- 16. Abstract Linear Algebra
- References
- Index