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3042100 
Journal Article 
Regularization and variable selection via the elastic net 
Zou, H; Hastie, T 
2005 
1 
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
ISSN: 1369-7412
EISSN: 1467-9868 
67 
301-320 
WOS:000227498200007 
We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together. The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the p>n case. An algorithm called LARS-EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lasso. 
grouping effect; LARS algorithm; Lasso; penalization; p >> n problem; variable selection