Applied Matrix and Tensor Variate Data Analysis (JSS Research Series in Statistics) - Tapa blanda

 
9784431553861: Applied Matrix and Tensor Variate Data Analysis (JSS Research Series in Statistics)
Tapa blanda
ISBN 10: 443155386X ISBN 13: 9784431553861
Editorial: Springer, 2016

Sinopsis

This book provides comprehensive reviews of recent progress in matrix variate and tensor variate data analysis from applied points of view. Matrix and tensor approaches for data analysis are known to be extremely useful for recently emerging complex and high-dimensional data in various applied fields. The reviews contained herein cover recent applications of these methods in psychology (Chap. 1), audio signals (Chap. 2) , image analysis from tensor principal component analysis (Chap. 3), and image analysis from decomposition (Chap. 4), and genetic data (Chap. 5) . Readers will be able to understand the present status of these techniques as applicable to their own fields. In Chapter 5 especially, a theory of tensor normal distributions, which is a basic in statistical inference, is developed, and multi-way regression, classification, clustering, and principal component analysis are exemplified under tensor normal distributions. Chapter 6 treats one-sided tests under matrix variate andtensor variate normal distributions, whose theory under multivariate normal distributions has been a popular topic in statistics since the books of Barlow et al. (1972) and Robertson et al. (1988). Chapters 1, 5, and 6 distinguish this book from ordinary engineering books on these topics.

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Críticas

“In its six chapters it covers a large span of methods and problems of eigenvector analysis of matrices, and many-way arrays, also known as tensors. Seven authors contribute to describing and developing these techniques for practical applications of computational statistical analysis in various fields of high-dimensional data. ... This monograph can serve to lecturers, graduate students, and researchers working with theoretical methods and numerical estimations in modern multivariate statistical analysis.” (Stan Lipovetsky, Technometrics, Vol. 58 (3), August, 2016)

Reseña del editor

This book provides comprehensive reviews of recent progress in matrix variate and tensor variate data analysis from applied points of view. Matrix and tensor approaches for data analysis are known to be extremely useful for recently emerging complex and high-dimensional data in various applied fields. The reviews contained herein cover recent applications of these methods in psychology (Chap. 1), audio signals (Chap. 2) , image analysis  from tensor principal component analysis (Chap. 3), and image analysis from decomposition (Chap. 4), and genetic data (Chap. 5) . Readers will be able to understand the present status of these techniques as applicable to their own fields.  In Chapter 5 especially, a theory of tensor normal distributions, which is a basic in statistical inference, is developed, and multi-way regression, classification, clustering, and principal component analysis are exemplified under tensor normal distributions. Chapter 6 treats one-sided tests under matrix variate and tensor variate normal distributions, whose theory under multivariate normal distributions has been a popular topic in statistics since the books of Barlow et al. (1972) and Robertson et al. (1988). Chapters 1, 5, and 6 distinguish this book from ordinary engineering books on these topics.

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Editorial
Springer
Año de publicación
2016
Idioma
Inglés
ISBN 10 
443155386X
ISBN 13 
9784431553861
Encuadernación
Tapa blanda
Número de edición
1
Número de páginas
148
Editor
Sakata Toshio
Contacto del fabricante
ProductSafety@springernature.com
ProductSafety@springernature.com

Poststr. 9
Darmstadt
64293
Alemania

Otras ediciones populares con el mismo título

9784431553885: Applied Matrix and Tensor Variate Data Analysis

Edición Destacada

ISBN 10:  4431553886 ISBN 13:  9784431553885
Editorial: Springer, 2016
Tapa blanda