Gregory C. Reinsel,Raja P. Velu,Kun Chen
Multivariate Reduced-Rank Regression: Theory, Methods and Applications
Multivariate Reduced-Rank Regression: Theory, Methods and Applications
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- More about Multivariate Reduced-Rank Regression: Theory, Methods and Applications
This book provides an account of multivariate reduced-rank regression, a tool of multivariate analysis with broad applications, and its connection to other statistical methods. It incorporates Big Data methodology and high-dimensional reduced-rank regression, and is designed for advanced students, practitioners, and researchers dealing with moderate and high-dimensional multivariate data.
Format: Paperback / softback
Length: 411 pages
Publication date: 01 December 2022
Publisher: Springer-Verlag New York Inc.
This comprehensive book delves into the realm of multivariate reduced-rank regression, a powerful tool in multivariate analysis with a wide range of applications. In addition to providing a historical overview of the topic, it explores its connections to other widely used statistical methods like multivariate analysis of variance (MANOVA), discriminant analysis, principal components, canonical correlation analysis, and errors-in-variables models.
The new edition of this book incorporates cutting-edge methodologies, including Big Data 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 presents theoretical developments alongside computational procedures, accompanied by numerical examples from diverse fields such as biochemistry, genetics, marketing, and finance.
Designed for advanced students, practitioners, and researchers dealing with moderate and high-dimensional multivariate data, this book offers a natural approach to analyzing large datasets. It serves as a valuable resource for seminar-type courses taken by advanced graduate students in statistics, machine learning, econometrics, business, and engineering.
By exploring the complexities of multivariate reduced-rank regression, this book provides a valuable resource for those seeking to advance their understanding of multivariate analysis and its practical applications.
Weight: 880g
Dimension: 235 x 155 (mm)
ISBN-13: 9781071627914
Edition number: 2nd ed. 2022
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