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