Alternate Procedures for Analysis of Multivariate Regression Models

  • Gregory C. Reinsel
  • Raja P. Velu
Part of the Lecture Notes in Statistics book series (LNS, volume 136)

Abstract

In previous chapters, we have developed various reduced-rank multivariate regression models, and indicated their usefulness in different applications as dimension-reduction tools. We now briefly survey and discuss some other related multivariate regression modeling methodologies that have similar parameter reduction objectives as reduced-rank regression, such as multivariate ridge regression, partial least squares, joint continuum regression, and other shrinkage and regularization techniques. Some of these procedures are designed particularly for situations where there is a very large number n of predictor variables relative to the sample size T including, for example, n > T.

Keywords

Canonical Correlation Analysis Ridge Regression Multivariate Regression Model Shrinkage Estimator Estimate Regression Equation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Gregory C. Reinsel
    • 1
  • Raja P. Velu
    • 2
  1. 1.Department of StatisticsUniversity of Wisconsin, MadisonMadisonUSA
  2. 2.School of ManagementSyracuse UniversitySyracuseUSA

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