# Matrix-Based Introduction to Multivariate Data Analysis [electronic resource] / by Kohei Adachi.

Author/Creator:
Publication:
Singapore : Springer Singapore : Imprint: Springer, 2020.
Format/Description:
Book
1 online resource (XIX, 457 pages) : 94 illustrations, 13 illustrations in color.
Edition:
2nd ed. 2020.
Series:
Mathematics and Statistics (SpringerNature-11649)
Contained In:
Springer Nature eBook
Status/Location:

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## Details

Subjects:
Statistics.
Mathematical statistics.
Local subjects:
Statistical Theory and Methods. (search)
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. (search)
Statistics for Social Sciences, Humanities, Law. (search)
Statistics and Computing/Statistics Programs. (search)
Statistics for Business, Management, Economics, Finance, Insurance. (search)
Probability and Statistics in Computer Science. (search)
System Details:
text file PDF
Summary:
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.
Contents:
Elementary matrix operations
Intravariable statistics
Inter-variable statistics
Regression analysis
Principal component analysis
Principal component.
Contributor: