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Titlebook: Multivariate Reduced-Rank Regression; Theory, Methods and Gregory C. Reinsel,Raja P. Velu,Kun Chen Book 2022Latest edition Springer Scienc

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书目名称Multivariate Reduced-Rank Regression
副标题Theory, Methods and
编辑Gregory C. Reinsel,Raja P. Velu,Kun Chen
视频video
概述Offers techniques that are particularly relevant for the analysis of large dimensional data sets.Provides guidance in offering elegant, simplified interpretations of data, as well as developing models
丛书名称Lecture Notes in Statistics
图书封面Titlebook: Multivariate Reduced-Rank Regression; Theory, Methods and  Gregory C. Reinsel,Raja P. Velu,Kun Chen Book 2022Latest edition Springer Scienc
描述.This book provides an account of multivariate reduced-rank regression, a tool of multivariate analysis that enjoys a broad array of applications. In addition to a historical review of the topic, its connection to other widely used statistical methods, such as multivariate analysis of variance (MANOVA), discriminant analysis, principal components, canonical correlation analysis, and errors-in-variables models, is also discussed...This new edition incorporates Big Data methodology and its applications, as well as high-dimensional reduced-rank regression, generalized reduced-rank regression with complex data, and sparse and low-rank regression methods. Each chapter contains developments of basic theoretical results, as well as details on computational procedures, illustrated with numerical examples drawn from disciplines such as biochemistry, genetics, marketing, and finance. ..This book is designed for advanced students, practitioners, and researchers, who may deal withmoderate and high-dimensional multivariate data. Because regression is one of the most popular statistical methods, the multivariate regression analysis tools described should provide a natural way of looking at large
出版日期Book 2022Latest edition
关键词high-dimensional reduced-rank regression; generalized reduced-rank regression; non-Gaussian; mixed data
版次2
doihttps://doi.org/10.1007/978-1-0716-2793-8
isbn_softcover978-1-0716-2791-4
isbn_ebook978-1-0716-2793-8Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer Science+Business Media, LLC, part of Springer Nature 2022
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https://doi.org/10.1007/978-1-0716-2793-8high-dimensional reduced-rank regression; generalized reduced-rank regression; non-Gaussian; mixed data
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Multivariate Reduced-Rank Regression978-1-0716-2793-8Series ISSN 0930-0325 Series E-ISSN 2197-7186
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