Regression Shrinkage and Selection via the

We propose the elastic net, a new regression shrinkage and selection method. Real data and a simulation study show that the elastic net often outperforms the lasso, while it enjoys a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strong correlated predictors are kept in the model. The elastic net is particularly useful in the analysis of microarray data in which the number of genes (predictors) is much bigger than the number of samples (observations). We show how the elastic net can be used to construct a classification rule and do automatic gene selection at the same time in microarray data, where the lasso is not very satisfied