Overview
- Enables even readers without knowledge of matrices to grasp their operations to learn multivariate data analysis in matrix forms
- Emphasizes what model underlies an analysis procedure and what function is optimized for estimating model parameters as the fastest way to understand the procedure
- Introduces plain numerical illustrations of the purposes for which procedures are utilized, followed by mathematical descriptions for an intuitive understanding of those purposes
- Includes supplementary material: sn.pub/extras
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Table of contents (17 chapters)
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Elementary Statistics with Matrices
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Least Squares Procedures
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Maximum Likelihood Procedures
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Miscellaneous Procedures
Keywords
About this book
This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on singular value decomposition among theorems in matrix algebra.
The book begins with an explanation of fundamental matrix operations and the matrix expressions of elementary statistics, followed by the introduction of popular multivariate procedures with advancing levels of matrix algebra chapter by chapter. This organization of the book allows readers without knowledge of matrices to deepen their understanding of multivariate data analysis.
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Bibliographic Information
Book Title: Matrix-Based Introduction to Multivariate Data Analysis
Authors: Kohei Adachi
DOI: https://doi.org/10.1007/978-981-10-2341-5
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2016
Softcover ISBN: 978-981-10-9595-5Published: 22 April 2018
eBook ISBN: 978-981-10-2341-5Published: 11 October 2016
Edition Number: 1
Number of Pages: XIII, 301
Number of Illustrations: 47 b/w illustrations, 8 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