Matrix Analysis for Statistics
eBook - PDF

Matrix Analysis for Statistics

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Matrix Analysis for Statistics

About this book

An up-to-date version of the complete, self-contained introduction to matrix analysis theory and practice

Providing accessible and in-depth coverage of the most common matrix methods now used in statistical applications, Matrix Analysis for Statistics, Third Edition features an easy-to-follow theorem/proof format. Featuring smooth transitions between topical coverage, the author carefully justifies the step-by-step process of the most common matrix methods now used in statistical applications, including eigenvalues and eigenvectors; the Moore-Penrose inverse; matrix differentiation; and the distribution of quadratic forms.

An ideal introduction to matrix analysis theory and practice, Matrix Analysis for Statistics, Third Edition features:

• New chapter or section coverage on inequalities, oblique projections, and antieigenvalues and antieigenvectors

• Additional problems and chapter-end practice exercises at the end of each chapter

• Extensive examples that are familiar and easy to understand

• Self-contained chapters for flexibility in topic choice

• Applications of matrix methods in least squares regression and the analyses of mean vectors and covariance matrices

Matrix Analysis for Statistics, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses on matrix methods, multivariate analysis, and linear models. The book is also an excellent reference for research professionals in applied statistics.

James R. Schott, PhD, is Professor in the Department of Statistics at the University of Central Florida. He has published numerous journal articles in the area of multivariate analysis. Dr. Schott's research interests include multivariate analysis, analysis of covariance and correlation matrices, and dimensionality reduction techniques.

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Yes, you can access Matrix Analysis for Statistics by James R. Schott in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Wiley
Year
2016
Print ISBN
9781119092483
eBook ISBN
9781119092476

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Dedication
  5. Contents
  6. Preface
  7. About the Companion Website
  8. Chapter 1 A Review of Elementary Matrix Algebra
  9. Chapter 2 Vector Spaces
  10. Chapter 3 Eigenvalues and Eigenvectors
  11. Chapter 4 Matrix Factorizations and Matrix Norms
  12. Chapter 5 Generalized Inverses
  13. Chapter 6 Systems of Linear Equations
  14. Chapter 7 Partitioned Matrices
  15. Chapter 8 Special Matrices and Matrix Operations
  16. Chapter 9 Matrix Derivatives and Related Topics
  17. Chapter 10 Inequalities
  18. Chapter 11 Some Special Topics Related to Quadratic Forms
  19. References
  20. Index
  21. EULA