Login

Gruber, Marvin H. J.

Matrix Algebra for Linear Models

Gruber, Marvin H. J. - Matrix Algebra for Linear Models, ebook

109,35€

Ebook, ePUB with Adobe DRM
ISBN: 9781118608814
DRM Restrictions

Printing118 pages with an additional page accrued every 7 hours, capped at 118 pages
Copy to clipboard5 excerpts

Matrix methods have evolved from a tool for expressing statistical problems to an indispensable part of the development, understanding, and use of various types of complex statistical analyses. This evolution has made matrix methods a vital part of statistical education. Traditionally, matrix methods are taught in courses on everything from regression analysis to stochastic processes, thus creating a fractured view of the topic. Matrix Algebra for Linear Models offers readers a unique, unified view of matrix analysis theory (where and when necessary), methods, and their applications. Written for future statisticians, both theoretical and applied, this book emphasizes the key topics that are needed in a concise and accurate way. Emphasis is on understanding and interpreting principal components as an eigenvalue, generalized inverses, and singular value decomposition. The derivation of important results in Analysis of Variance (ANOVA) is made elegant by the use of some of the properties of quadratic forms, the Kronecker product, and special matrices. A large number of numerical examples and exercises are included to further illustrate the motivation behind the concepts.

Keywords: Applied Probability & Statistics - Models

Author(s)
Publisher
John Wiley and Sons, Inc.
Publication year
2014
Language
en
Edition
1
Page amount
392 pages
Category
Natural Sciences
Format
Ebook
eISBN (ePUB)
9781118608814
Printed ISBN
9781118592557

Similar titles