Multivariate Reduced-Rank Regression (eBook)

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 with moderate 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 (both cross-sectional and chronological) data sets. This book can be assigned in seminar-type courses taken by advanced graduate students in statistics, machine learning, econometrics, business, and engineering.

Raja P. Velu taught Analytics and Finance at Whitman School of Management, Syracuse University. The first version of the book was primarily based on his thesis written under the supervision of Professors Reinsel and Wichern. He works in the area of Big Data modelling with interest in Time Series and Forecasting applications. His latest book, Algorithmic Trading and Quantitative Strategies, co-authored with practitioners from Barclays and JP Morgan Chase is recently published by Taylor and Francis. He has been affiliated with the tech industry for over twenty years. Currently he is a Visiting Researcher at Google Inc.

Kun Chen is on the faculty of the Department of Statistics at the University of Connecticut . His main research areas include multivariate statistical learning, machine learning, statistical computing, and healthcare analytics with large-scale data. The first version of the book has had profound influence on his research since his graduate study at the University of Iowa under the supervision of Professor Kung-Sik Chan. In recent years, he has been pursuing a comprehensive investigation of theory, methodologies, and computational approaches of large-scale reduced-rank modelling. His related work has been supported by several grants from NSF and NIH and has resulted in more than twenty publications in top statistics and machine learning journals.

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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 (both cross-sectional and chronological) data sets. This book can be assigned in seminar-type courses taken by advanced graduate students in statistics, machine learning, econometrics, business, and engineering.