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Latent Factor Prediction Pursuit for Rank Deficient Regressors

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  • Luebke, Karsten
  • Czogiel, Irina
  • Weihs, Claus

Abstract

In simulation studies Latent Factor Prediction Pursuit outperformed classical reduced rank regression methods. The algorithm described so far for Latent Factor Prediction Pursuit had two shortcomings: It was only implemented for situations where the explanatory variables were of full colum rank. Also instead of the projection matrix only the regression matrix was calculated. These problems are addressed by a new algorithm which finds the prediction optimal projection.

Suggested Citation

  • Luebke, Karsten & Czogiel, Irina & Weihs, Claus, 2004. "Latent Factor Prediction Pursuit for Rank Deficient Regressors," Technical Reports 2004,75, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200475
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    References listed on IDEAS

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    1. Weihs, Claus & Hothorn, Torsten, 2002. "Determination of optimal prediction oriented multivariate latent factor models using loss functions," Technical Reports 2002,15, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Eddelbuttel, Dirk, 1996. "Object-Oriented Econometrics: Matrix Programming in C++ Using GCC and NEWMAT," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(2), pages 199-209, March-Apr.
    3. Almoy, Trygve, 1996. "A simulation study on comparison of prediction methods when only a few components are relevant," Computational Statistics & Data Analysis, Elsevier, vol. 21(1), pages 87-107, January.
    4. Leo Breiman & Jerome H. Friedman, 1997. "Predicting Multivariate Responses in Multiple Linear Regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 3-54.
    5. Weihs, Claus & Luebke, Karsten, 2004. "A Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classification," Technical Reports 2004,29, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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