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Multivariate reduced-rank regressiontheory, methods and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multivariate reduced-rank regressionby Gregory C. Reinsel, Raja P. Velu, Kun Chen.
其他題名:	theory, methods and applications /
作者:	Reinsel, Gregory C.
其他作者:	Velu, Rajabather Palani.
出版者:	New York, NY :Springer New York :2022.
面頁冊數:	xxi, 411 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Regression analysis.
電子資源:	https://doi.org/10.1007/978-1-0716-2793-8
ISBN:	9781071627938$q(electronic bk.)

Multivariate reduced-rank regressiontheory, methods and applications /
Reinsel, Gregory C.

Multivariate reduced-rank regressiontheory, methods and applications /[electronic resource] :by Gregory C. Reinsel, Raja P. Velu, Kun Chen. - Second edition. - New York, NY :Springer New York :2022. - xxi, 411 p. :ill., digital ;24 cm. - Lecture notes in statistics,v. 2252197-7186 ;. - Lecture notes in statistics ;205..

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.

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.

ISBN: 9781071627938$q(electronic bk.)

Standard No.: 10.1007/978-1-0716-2793-8doiSubjects--Topical Terms:

181872
Regression analysis.

LC Class. No.: QA278.2

Dewey Class. No.: 519.5

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505 0 $a 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.
520 $a 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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