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Reduced-Rank Regression Models With Two Sets of Regressors

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

Abstract

In Chapter 2 we have demonstrated the utility of the reduced-rank model for analyzing data on a large number of variables. The interrelationship between the dependent and independent variables can be explained parsimoniously through the assumption of a lower rank for the regression coefficient matrix. The basic model that was described in Chapter 2 assumes that the predictor variables are all grouped into one set and therefore, they are all subject to the same canonical calculations. In this chapter we broaden the scope of the reduced-rank model by entertaining the possibility that the predictor variables can be divided into two distinct sets with separate reduced-rank structures. Such an extension will be shown to have some interesting applications.

Keywords

Linear Discriminant Analysis Error Covariance Matrix Alternative Estimator Linear Discriminant Function Asymptotic Covariance Matrix 
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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