Multivariate reduced-rank regression. Theory, methods and applications. 2nd edition. (English) Zbl 1515.62007
Lecture Notes in Statistics 225. New York, NY: Springer (ISBN 978-1-0716-2791-4/pbk; 978-1-0716-2793-8/ebook). xxi, 411 p. (2022).
Publisher’s description: 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.
See the review of the first edition in [Zbl 0909.62066].
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.
See the review of the first edition in [Zbl 0909.62066].
MSC:
62-01 | Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics |
62J05 | Linear regression; mixed models |
62-02 | Research exposition (monographs, survey articles) pertaining to statistics |
62J10 | Analysis of variance and covariance (ANOVA) |
62H20 | Measures of association (correlation, canonical correlation, etc.) |
62H25 | Factor analysis and principal components; correspondence analysis |
62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |
62R07 | Statistical aspects of big data and data science |