Cited by 20,957Statistical Methods and InferenceAdvanced Statistical Methods and ModelsSparse and Compressive Sensing Techniques

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Regularization and Variable Selection Via the Elastic Net

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Zou, Hui, Hastie, Trevor·2005·Journal of the Royal Statistical Society Series B: Statistical Methodology

DOI 10.1111/j.1467-9868.2005.00503.x

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Abstract

Summary: 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.

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Authors: Zou, H., & Hastie, T.
Affiliations: Stanford University (US)
Corresponding author: Hastie, Trevor
ORCID: Zou, Hui
Hastie, Trevor
Venue: Journal of the Royal Statistical Society Series B: Statistical Methodology
Publisher: Oxford University Press (OUP)
Published: 2005-03-09
Volume: 67
Issue: 2
Pages: 301-320
ISSN: 1369-7412 (ISSN-L)
1467-9868
DOI: 10.1111/j.1467-9868.2005.00503.x
OpenAlex: W2122825543
Cited by: 20,957
References: 7
Citations by year: 2026: 1933 · 2025: 1906 · 2024: 1653 · 2023: 1540 · 2022: 1664 · 2021: 1924 · 2020: 1702 · 2019: 1536 · 2018: 1338 · 2017: 1092 · 2016: 949 · 2015: 884 · 2014: 727 · 2013: 585 · 2012: 468
FWCI: 79.40
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor, 6603 d)
Locations: Journal of the Royal Statistical Society Series B (Statistical Methodology) (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: National Science Foundation (DMS-0204162)
National Institutes of Health (RO1-EB0011988-08)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
Physical Sciences › Computer Science › Signal Processing › Sparse and Compressive Sensing Techniques
Keywords: elastic net, regularization, feature selection, LASSO, high-dimensional data, correlated predictors, grouping effect, regularization path, sparse representation

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Regularization and Variable Selection Via the Elastic Net

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