Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31101
Authors: 
Tutz, Gerhard
Ulbricht, Jan
Year of Publication: 
2006
Series/Report no.: 
Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 486
Abstract: 
A new regularization method for regression models is proposed. The criterion to be minimized contains a penalty term which explicitly links strength of penalization to the correlation between predictors. As the elastic net, the method encourages a grouping effect where strongly correlated predictors tend to be in or out of the model together. A boosted version of the penalized estimator, which is based on a new boosting method, allows to select variables. Real world data and simulations show that the method compares well to competing regularization techniques. In settings where the number of predictors is smaller than the number of observations it frequently performs better than competitors, in high dimensional settings prediction measures favor the elastic net while accuracy of estimation and stability of variable selection favors the newly proposed method.
Subjects: 
Correlation based estimator
Boosting
Variable selection
Elastic net
Lasso
Penalization
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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