Introduction. Vectors and Matrices. Rank of Matrices. Determinants. Inverses. Eigenanalysis of Real Symmetric Matrices. Vector and Matrix Calculus. Further Topics. Key Applications to Statistics. Outline Solutions to Exercises. Bibliography. Index.
Dr. Nick Fieller is a retired senior lecturer in the School of Mathematics and Statistics and an honorary research fellow in archaeology at the University of Sheffield. His research interests include multivariate data analysis and statistical modeling in the pharmaceutical industry, archaeology, and forensic sciences.
"…belongs to the category of mathematics books that integrate a
programming language with substantive content. On the substantive
side, the author has meticulously selected matrix algebra topics
that are fundamental to learning, using, and understanding
statistics. In this manner, the reader is saved time by focusing on
matrix mathematics which is of most relevance to statistics. In
addition, an instructor also benefits from the concise introduction
to matrix algebra related to statistics. Therefore, this book can
easily be adopted as a matrix algebra supplemental book in a
syllabus on statistics. The exercises are short but rigorous, with
detailed solutions provided at the end of the book...as a
traditional text to teach practical matrix algebra to students
taking multivariate and more advanced statistics courses, this book
can be of good use."
—Abdolvahab Khademi, University of Massachusetts, Journal of
Statistical Software, July 2016"Key features of the book include
highlighting useful tricks when manipulating matrices, derivation
of key results with step-by-step cross-referenced explanations and
demonstrations of implementing the techniques in R using numerical
examples…it is a good beginner’s guide to understanding and
manipulating matrices in R. It is suitable for early year
undergraduate students and anyone who wishes to be introduced to
matrix algebra in R in preparation for high-level or specialised
studies in statistics. The book’s collection of summaries and key
results also make it a good handbook for any statistician to refer
to."
—Shuangzhe Liu, Stastistical Papers, July 2016 "… a concise and
straightforward presentation of matrix algebra techniques that are
commonly used in statistics. Furthermore, the book discusses how to
implement numerical instances of these techniques using R. … If you
have a need or desire to carry out matrix computations in R, then
it is likely that here you will find the needed commands. There are
several nice features … it is very easy to find the R command for
carrying out a specific matrix calculation. … useful as a
reference. In addition, the author provides helpful tips and tricks
for working with R. Another positive feature of this book is the
applications to statistics. … the inclusion of exercises
facilitates the use of this book as a course text."
—MAA Reviews, January 2016
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