Latent Factor Prediction Pursuit for Rank Deficient Regressors

Karsten Luebke, Irina Czogiel and Claus Weihs

No 2004,75, Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen

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.

Keywords: simulated annealing; prediction oriented projections; reduced rank regression; rank deficient regressors; simulation study (search for similar items in EconPapers)
Date: 2004
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