Elastic Net Procedure for Partially Linear Models
Methodology
2015-07-23 v1 Probability
Machine Learning
Abstract
Variable selection plays an important role in the high-dimensional data analysis. However the high-dimensional data often induces the strongly correlated variables problem. In this paper, we propose Elastic Net procedure for partially linear models and prove the group effect of its estimate. By a simulation study, we show that the strongly correlated variables problem can be better handled by the Elastic Net procedure than Lasso, ALasso and Ridge. Based on an empirical analysis, we can get that the Elastic Net procedure is particularly useful when the number of predictors is much bigger than the sample size .
Cite
@article{arxiv.1507.06032,
title = {Elastic Net Procedure for Partially Linear Models},
author = {Chunhong Li and Dengxiang Huang and Hongshuai Dai and Xinxing Wei},
journal= {arXiv preprint arXiv:1507.06032},
year = {2015}
}
Comments
arXiv admin note: text overlap with arXiv:0908.1836 by other authors