2015/07/22 by Chunhong Li, Li, Chunhong, Dengxiang Huang +5
Computer Science · Engineering · Mathematics · #Advanced Statistical Methods and Models #Algorithm #Applied mathematics #Artificial intelligence #Computer science #Elastic net regularization #Engineering #Feature selection #Finite element method #Geometry #Lasso (programming language) #Linear elasticity #Mathematical analysis #Mathematical optimization #Mathematics #Net (polyhedron) #Neural Networks and Applications #Physics #Sample (material) #Statistical Methods and Inference #Structural engineering #Thermodynamics #Variable (mathematics) #math.PR #stat.ME #stat.ML
paper · pdf · doi:10.48550/arxiv.1507.06032
published in arXiv (Cornell University) (Cornell University) · arXiv admin note: text overlap with arXiv:0908.1836 by other authors
arxiv created 2015/07/22 · openalex publication_date 2015/07/22 · arxiv updated 2015/07/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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 p is much bigger than the sample size n.