Matrix-Based Introduction to Multivariate Data Analysis

Adachi

ISBN 10: 9811541027 ISBN 13: 9789811541025

Edit� par Springer, 2020

Neuf(s) Couverture rigide

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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.

� propos de l?auteur: Kohei Adachi, Graduate School of Human Sciences, Osaka University

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

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Titre : Matrix-Based Introduction to Multivariate ...
�diteur : Springer
Date d'�dition : 2020
Reliure : Couverture rigide
Etat : New
Edition : 2�me �dition

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Matrix-Based Introduction to Multivariate Data Analysis

Kohei Adachi

Edit� par Springer Nature Singapore, 2020

ISBN 10 : 9811541027 ISBN 13 : 9789811541025

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Allows even readers with no knowledge of matrices to understand the operations for multivariate data analysisHighlights understanding which function is optimized to obtain a solution as the fastest way to capture a procedureDemonstrates mul. N� de r�f. du vendeur 353803003

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Matrix-Based Introduction to Multivariate Data Analysis

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Matrix-Based Introduction to Multivariate Data Analysis

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Matrix-Based Introduction to Multivariate Data Analysis

Kohei Adachi

Edit� par Springer Nature Singapore, Springer Nature Singapore Mai 2020, 2020

ISBN 10 : 9811541027 ISBN 13 : 9789811541025

Neuf Couverture rigide

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Buch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 480 pp. Englisch. N� de r�f. du vendeur 9789811541025

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Matrix-Based Introduction to Multivariate Data Analysis

Kohei Adachi

Edit� par Springer Nature Singapore, Springer Nature Singapore Mai 2020, 2020

ISBN 10 : 9811541027 ISBN 13 : 9789811541025

Neuf Couverture rigide

impression � la demande

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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 480 pp. Englisch. N� de r�f. du vendeur 9789811541025

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Matrix-Based Introduction to Multivariate Data Analysis

Kohei Adachi

Edit� par Springer Nature Singapore, Springer Nature Singapore, 2020

ISBN 10 : 9811541027 ISBN 13 : 9789811541025

Neuf Couverture rigide

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N� de r�f. du vendeur 9789811541025

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