Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/22588
Authors: 
Luebke, Karsten
Czogiel, Irina
Weihs, Claus
Year of Publication: 
2004
Series/Report no.: 
Technical Report / Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen 2004,75
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.
Subjects: 
simulated annealing
prediction oriented projections
reduced rank regression
rank deficient regressors
simulation study
Document Type: 
Working Paper

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