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Matrix-Based Introduction to Multivariate Data Analysis

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Matrix-Based Introduction to Multivariate Data Analysis/ by Kohei Adachi.
作者:	Adachi, Kohei.
面頁冊數:	XIX, 457 p. 94 illus., 13 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Probability and Statistics in Computer Science. -
電子資源:	https://doi.org/10.1007/978-981-15-4103-2
ISBN:	9789811541032

Matrix-Based Introduction to Multivariate Data Analysis
Adachi, Kohei.

Matrix-Based Introduction to Multivariate Data Analysis[electronic resource] /by Kohei Adachi. - 2nd ed. 2020. - XIX, 457 p. 94 illus., 13 illus. in color.online resource.

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 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.

ISBN: 9789811541032

Standard No.: 10.1007/978-981-15-4103-2doiSubjects--Topical Terms:

669886
Probability and Statistics in Computer Science.

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

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