Multivariate Reduced-Rank Regression: Theory and Applications (Lecture Notes in Statistics)

This book provides an account of the theory and applications of multivariate reduced-rank regression, a tool of multivariate analysis that recently has come into increased use in broad areas 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. Each chapter contains developments of basic theoretical results as well as details on computational procedures and other practical matters, illustrated with numerical examples drawn from disciplines such as biochemistry, marketing, and finance. This book attempts to bring together, for the first time, the scope and range of the tool of multivariate reduced- rank regression, which has been in existence in varied forms for nearly fifty years. This book should appeal to both 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 in this book should provide a natural way of looking at large data sets. This book can be ideally used for seminar-type courses taken by advanced graduate students in statistics, econometrics, business, and engineering. Gregory C. Reinsel is Professor of Statistics at the University of Wisconsin, Madison. He is a Fellow of the American Statistical Association. He is 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. Raja P. Velu is on the faculty of the School of Management at Syracuse