Latent Factor Prediction Pursuit for Rank Deficient Regressors

dc.contributor.author	Czogiel, I.	de
dc.contributor.author	Luebke, K.	de
dc.contributor.author	Weihs, C.	de
dc.date.accessioned	2005-01-31T08:15:30Z
dc.date.available	2005-01-31T08:15:30Z
dc.date.issued	2004	de
dc.description.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.	en
dc.format.extent	147555 bytes
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/2003/20089
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15681
dc.language.iso	en	de
dc.publisher	Universität Dortmund	de
dc.subject.ddc	310	de
dc.title	Latent Factor Prediction Pursuit for Rank Deficient Regressors	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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