Multivariate Reduced-Rank Regression

1. Multivariate Linear Regression.- 2. Reduced-Rank Regression Model.- 3. Reduced-Rank Regression Models with Two Sets of Regressors.- 4. Reduced-Rank Regression Model with Autoregressive Errors.- 5. Multiple Time Series Modeling with Reduced Ranks.- 6. The Growth Curve Model and Reduced-Rank Regression Methods.- 7. Seemingly Unrelated Regression Models with Reduced Ranks.- 8. Applications of Reduced-Rank Regression in Financial Economics.- 9. High-Dimensional Reduced-Rank Regression.- 10. Generalized Reduced-Rank Regression with Complex Data.- 11. Sparse and Low-Rank Regression. 12. Alternate Procedures for Analysis of Multivariate Regression Models.

Gregory C. Reinsel (now deceased) was Professor of Statistics at the University of Wisconsin, Madison. He was a fellow of the American Statistical Association. He also author of the book Elements of Multivariate Time Series Analysis, Second Edition, and coauthor, with G.E.P. Box and G.M. Jenkins, of the book Time Series Analysis: Forecasting and Control, Third Edition. Greg will remain the first author, in our gratitude.

Raja P. Velu taught business analytics and finance at Syracuse University. The first version of the book was mainly based on his thesis written under the supervision of Professor Reinsel and Professor Dean Wichern. He works in the big data models area with interest in high-dimensional time series and forecasting applications. His book, Algorithmic Trading and Quantitative Strategies, co-authored with practitioners from CITI and JP Morgan Chase, is published by Taylor and Francis. He was recently (2021-2022) a visiting researcher at Google working with the Resource Efficiency Data Science team.

Kun Chen is an associate professor in the Department of Statistics at the University of Connecticut. He is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. The first version of the book has had profound influence on his research since his PhD study at the University of Iowa under the supervision of Professor Kung-Sik Chan. His related work has resulted in many publications in statistics, machine learning, and scientific journals and the developed methods have been applied to tackle consequential problems in various fields including public health, ecology, and biological sciences.