Table of contents
About this book
This is the first textbook that allows readers who may be unfamiliar with matrices to understand a variety of multivariate analysis procedures in matrix forms. By explaining which models underlie particular procedures and what objective function is optimized to fit the model to the data, it enables readers to rapidly comprehend multivariate data analysis. Arranged so that readers can intuitively grasp the purposes for which multivariate analysis procedures are used, the book also offers clear explanations of those purposes, with numerical examples preceding the mathematical descriptions.Supporting the modern matrix formulations by highlighting singular value decomposition among theorems in matrix algebra, this book is useful for undergraduate students who have already learned introductory statistics, as well as for graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis.
The book begins by explaining fundamental matrix operations and the matrix expressions of elementary statistics. Then, it offers an introduction to popular multivariate procedures, with each chapter featuring increasing advanced levels of matrix algebra.
Further the book includes in six chapters on advanced procedures, covering advanced matrix operations and recently proposed multivariate procedures, such as sparse estimation, together with a clear explication of the differences between principal components and factor analyses solutions. In a nutshell, this book allows readers to gain an understanding of the latest developments in multivariate data science.
- Book Title Matrix-Based Introduction to Multivariate Data Analysis
- DOI https://doi.org/10.1007/978-981-15-4103-2
- Copyright Information Springer Nature Singapore Pte Ltd. 2020
- Publisher Name Springer, Singapore
- eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
- Hardcover ISBN 978-981-15-4102-5
- Softcover ISBN 978-981-15-4105-6
- eBook ISBN 978-981-15-4103-2
- Edition Number 2
- Number of Pages XIX, 457
- Number of Illustrations 81 b/w illustrations, 13 illustrations in colour
Statistical Theory and Methods
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Statistics for Social Sciences, Humanities, Law
Statistics and Computing/Statistics Programs
Statistics for Business, Management, Economics, Finance, Insurance
Probability and Statistics in Computer Science
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