Matrix-Based Introduction to Multivariate Data Analysis

Kohei Adachi

ISBN 10: 9811541027 / ISBN 13: 9789811541025

Published by Springer Singapore, 2020

About this Item

Matrix-Based Introduction to Multivariate Data Analysis | Kohei Adachi | Buch | xix | Englisch | 2020 | Springer Singapore | EAN 9789811541025 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Seller Inventory # 118053895

Bibliographic Details

• Title: Matrix-Based Introduction to Multivariate Data Analysis
• Publisher: Springer Singapore
• Publication Date: 2020
• Language: English
• ISBN 10: 9811541027
• ISBN 13: 9789811541025
• Binding: Buch
• Edition: 2nd Edition
• Condition: Neu
• Dimensions: 241W x 160H x 32D millimeters 241 millimeters width by 160 millimeters height by 32 millimeters depth
• Weight: 881 grams
• Seller catalogs:
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About this title

Synopsis

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.

About the Author

Kohei Adachi, Graduate School of Human Sciences, Osaka University