Lecture Notes in Statistics

Multivariate Reduced-Rank Regression

Theory and Applications

Authors: Velu, Raja, Reinsel, Gregory C.

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About this book

In the area of multivariate analysis, there are two broad themes that have emerged over time. The analysis typically involves exploring the variations in a set of interrelated variables or investigating the simultaneous relationĀ­ ships between two or more sets of variables. In either case, the themes involve explicit modeling of the relationships or dimension-reduction of the sets of variables. The multivariate regression methodology and its variants are the preferred tools for the parametric modeling and descriptive tools such as principal components or canonical correlations are the tools used for addressing the dimension-reduction issues. Both act as complementary to each other and data analysts typically want to make use of these tools for a thorough analysis of multivariate data. A technique that combines the two broad themes in a natural fashion is the method of reduced-rank regresĀ­ sion. This method starts with the classical multivariate regression model framework but recognizes the possibility for the reduction in the number of parameters through a restrietion on the rank of the regression coefficient matrix. This feature is attractive because regression methods, whether they are in the context of a single response variable or in the context of several response variables, are popular statistical tools. The technique of reducedĀ­ rank regression and its encompassing features are the primary focus of this book. The book develops the method of reduced-rank regression starting from the classical multivariate linear regression model.

Table of contents (9 chapters)

  • Multivariate Linear Regression

    Reinsel, Gregory C. (et al.)

    Pages 1-14

  • Reduced-Rank Regression Model

    Reinsel, Gregory C. (et al.)

    Pages 15-55

  • Reduced-Rank Regression Models With Two Sets of Regressors

    Reinsel, Gregory C. (et al.)

    Pages 57-92

  • Reduced-Rank Regression Model With Autoregressive Errors

    Reinsel, Gregory C. (et al.)

    Pages 93-111

  • Multiple Time Series Modeling With Reduced Ranks

    Reinsel, Gregory C. (et al.)

    Pages 113-154

Buy this book

eBook $84.99
price for USA (gross)
  • ISBN 978-1-4757-2853-8
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $109.00
price for USA
  • ISBN 978-0-387-98601-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Multivariate Reduced-Rank Regression
Book Subtitle
Theory and Applications
Authors
Series Title
Lecture Notes in Statistics
Series Volume
136
Copyright
1998
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4757-2853-8
DOI
10.1007/978-1-4757-2853-8
Softcover ISBN
978-0-387-98601-2
Series ISSN
0930-0325
Edition Number
1
Number of Pages
XIII, 258
Topics