Overview
- Reviews applications of matrix and tensor variate data analysis by world-leading researchers in several representative applied fields including, psychology, audio signals, image data and genetics
- Treats the most important concepts of tensor principal component analysis in details
- The first book-length review of multivariate statistical inference under tensor normal distributions
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)
Part of the book sub series: JSS Research Series in Statistics (JSSRES)
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Table of contents (6 chapters)
Reviews
“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)
Editors and Affiliations
Bibliographic Information
Book Title: Applied Matrix and Tensor Variate Data Analysis
Editors: Toshio Sakata
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-4-431-55387-8
Publisher: Springer Tokyo
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s) 2016
Softcover ISBN: 978-4-431-55386-1Published: 10 February 2016
eBook ISBN: 978-4-431-55387-8Published: 02 February 2016
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: XI, 136
Number of Illustrations: 13 b/w illustrations, 23 illustrations in colour
Topics: Statistics and Computing/Statistics Programs, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Statistics for Social Sciences, Humanities, Law