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Regularization and variable selection via the elastic net


  • Hui Zou
  • Trevor Hastie


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. Copyright 2005 Royal Statistical Society.

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  • Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320.
  • Handle: RePEc:bla:jorssb:v:67:y:2005:i:2:p:301-320

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    References listed on IDEAS

    1. Simon French, 2003. "Modelling, making inferences and making decisions: The roles of sensitivity analysis," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 11(2), pages 229-251, December.
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