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Multivariate Reduced-Rank Regression

- Theory, Methods and Applications
Af: Kun Chen, Raja P. Velu, Gregory C. Reinsel Engelsk Paperback

Multivariate Reduced-Rank Regression

- Theory, Methods and Applications
Af: Kun Chen, Raja P. Velu, Gregory C. Reinsel Engelsk Paperback
Tjek vores konkurrenters priser

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.


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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 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.


Produktdetaljer
Sprog: Engelsk
Sider: 411
ISBN-13: 9781071627914
Indbinding: Paperback
Udgave:
ISBN-10: 1071627910
Udg. Dato: 1 dec 2022
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer-Verlag New York Inc.
Oplagsdato: 1 dec 2022
Forfatter(e) Kun Chen, Raja P. Velu, Gregory C. Reinsel


Kategori Sandsynlighedsregning og statistik


ISBN-13 9781071627914


Sprog Engelsk


Indbinding Paperback


Sider 411


Udgave


Længde 0mm


Bredde 155mm


Højde 235mm


Udg. Dato 1 dec 2022


Oplagsdato 1 dec 2022


Forlag Springer-Verlag New York Inc.