Variable selection plays an important role in high-dimensional data analysis.But the high-dimensional data often induces the strongly correlated variables problem,which should be properly handled. In this paper, we propose Elastic Net procedure for partially linear models and prove the group effect of its estimate. A simulation study shows that the Elastic Net procedure deals with the strongly correlated variables problem better than the Lasso, ALasso and the Ridge do. Based on the real world data study,we can get that the Elastic Net procedure is particularly useful when the number of predictors p is much bigger than the sample size n.

Variable selection plays an important role in high-dimensional data analysis.But the high-dimensional data often induces the strongly correlated variables problem,which should be properly handled. In this paper, we propose Elastic Net procedure for partially linear models and prove the group effect of its estimate. A simulation study shows that the Elastic Net procedure deals with the strongly correlated variables problem better than the Lasso, ALasso and the Ridge do. Based on the real world data study,we can get that the Elastic Net procedure is particularly useful when the number of predictors p is much bigger than the sample size n.