G-2013-109
Generalized Elastic Net Regression
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This work presents a variation of the elastic net penalization method. We propose applying a combined \(l\)
1 and \(l\)
2 norm penalization on a linear combination of regression parameters. This approach is an alternative to the \(l\)
1-penalization for variable selection, but takes care of the correlation between the linear combination of parameters. We devise a path algorithm fitting method similar to the one proposed for the least angle regression. Furthermore, a one-shot estimation technique of \(l\)
2 regularization parameter is proposed as an alternative to cross-validation. A simulation study is conducted to check the validity of the new technique.
Paru en décembre 2013 , 10 pages
Publication
jan. 2013
Generalized elastic net regression
, et
JSM Proceedings, 3457–3464, 2013
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