Overview
- Allows even readers with no knowledge of matrices to understand the operations for multivariate data analysis
- Highlights understanding which function is optimized to obtain a solution as the fastest way to capture a procedure
- Demonstrates multivariate procedures with numerical illustrations so that readers can intuitively grasp their usefulness
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (22 chapters)
-
Front Matter
-
Elementary Statistics with Matrices
-
Front Matter
-
-
Least Squares Procedures
-
Front Matter
-
-
Maximum Likelihood Procedures
-
Front Matter
-
-
Miscellaneous Procedures
-
Front Matter
-
Keywords
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 explainingfundamental 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.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Matrix-Based Introduction to Multivariate Data Analysis
Authors: Kohei Adachi
DOI: https://doi.org/10.1007/978-981-15-4103-2
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2020
Hardcover ISBN: 978-981-15-4102-5Published: 21 May 2020
Softcover ISBN: 978-981-15-4105-6Published: 22 May 2021
eBook ISBN: 978-981-15-4103-2Published: 20 May 2020
Edition Number: 2
Number of Pages: XIX, 457
Number of Illustrations: 81 b/w illustrations, 13 illustrations in colour
Topics: 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