Matrix-Based Introduction to Multivariate Data Analysis. 2nd ed. 2020

種類:

電子ブック

責任表示:

by Kohei Adachi

出版情報:

Singapore : Springer Nature Singapore : Imprint: Springer, 2020

著者名:

• Adachi, Kohei.
• SpringerLink (Online service)

ISBN:

9789811541032 [9811541035]    

注記:

Elementary matrix operations -- Intravariable statistics -- Inter-variable statistics -- Regression analysis -- Principal component analysis -- Principal component.
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

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