| 期刊全称 | Applied Matrix and Tensor Variate Data Analysis | | 影响因子2023 | Toshio Sakata | | 视频video | http://file.papertrans.cn/160/159949/159949.mp4 | | 发行地址 | 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.Trea | | 学科分类 | SpringerBriefs in Statistics | | 图书封面 |  | | 影响因子 | 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). Ch | | Pindex | Book 2016 |
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