Multivariate reduced-rank regression, theory, methods and applications
Reinsel, Gregory C.
Multivariate reduced-rank regression : theory, methods and applications [Documento eletrónico] / by Gregory C. Reinsel, Raja P. Velu, Kun Chen. - 2nd ed. . - New York, NY : Springer New York : Springer , 2022 . - XXI, 411 p. : il.. - (Lecture Notes in Statistics) ; 225 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.
Clicar aqui para aceder a um recurso externo
ISBN 978-1-0716-2793-8
Statistics
LCC QA276-280
Multivariate reduced-rank regression : theory, methods and applications [Documento eletrónico] / by Gregory C. Reinsel, Raja P. Velu, Kun Chen. - 2nd ed. . - New York, NY : Springer New York : Springer , 2022 . - XXI, 411 p. : il.. - (Lecture Notes in Statistics) ; 225 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.
Clicar aqui para aceder a um recurso externo
ISBN 978-1-0716-2793-8
Statistics
LCC QA276-280
