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Record Nr. |
UNINA9910633916903321 |
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Autore |
Reinsel Gregory C. |
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Titolo |
Multivariate Reduced-Rank Regression : Theory, Methods and Applications / / by Gregory C. Reinsel, Raja P. Velu, Kun Chen |
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Pubbl/distr/stampa |
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New York, NY : , : Springer New York : , : Imprint : Springer, , 2022 |
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ISBN |
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Edizione |
[2nd ed. 2022.] |
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Descrizione fisica |
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1 online resource (420 pages) : illustrations |
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Collana |
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Lecture Notes in Statistics, , 2197-7186 ; ; 225 |
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Classificazione |
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Disciplina |
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Soggetti |
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Statistics |
Statistical Theory and Methods |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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 withmoderate 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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