Книга Matrix-Based Introduction to Multivariate Data Analysis KOHEI ADACHI

Matrix-Based Introduction to Multivariate Data Analysis

Автор: KOHEI ADACHI
Език: Английски език
Корици: С меки корици
Издател: Springer Verlag, Singapore
Наличност: Външен склад
Изпращаме след 5-8 дни
82.61 161.57 лв
This book enables readers who may not be familiar with matrices to understand a variety of multivari...

Информация за книгата

Автор
Език
Английски език
Корици
Книга - С меки корици
Издадена
2018
страници
301
EAN
9789811095955
ISBN
9789811095955
Enbook ID
19416754
Теглоt
486
Размери
155 x 235 x 15

Пълно описание

This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter. 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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