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Multivariate Reduced-Rank Regression Theory, Methods and Applications

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 (MANOV...

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Bibliographic Details
Main Authors: Reinsel, Gregory C., Velu, Raja P. (Author), Chen, Kun (Author)
Format: eBook
Language:English
Published: New York, NY Springer New York 2022, 2022
Edition:2nd ed. 2022
Series:Lecture Notes in Statistics
Subjects:
Statistical Theory And Methods
Statistics 
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • 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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