The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Regularization and Variable Selection Via the Elastic Net
Open accessAbstract
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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“For example, if there are disproportionately few samples relative to the number of features in the dataset or if some of the input features are sparsely represented, early or late fusion is preferred because they can be implemented with traditional machine learning algorithms (e.g., Lasso and ElasticNet47) that are better suited for this type of data48.”
From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [47]
In the citing paper
“We used the elastic net algorithm, given substantial evidence that penalization improves prediction accuracy,25–27 alongside its well-established accuracy and robustness28,29 and ability to maintain clinical interpretability compared with less transparent machine learning algorithms (eg, random forests, neural networks).”
From A pilot study using frequent inpatient assessments of suicidal thinking to predict short-term postdischarge suicidal behavior (2021) · cites this paper as [29]
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Self-citationGene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Sparse Principal Component Analysis
Zou, H., Hastie, T., & Tibshirani, R. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x113430 · Cited by 3,152
Self-citationVariable Selection via Gibbs Sampling
George, E. I., & McCulloch, R. E. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476353 · Cited by 2,828
'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns
Hastie, T., Tibshirani, R., Eisen, M. B., Alizadeh, A., Levy, R., Staudt, L., Chan, W. C., Botstein, D., & Brown, P. (2000) · Genome Biology · doi:10.1186/gb-2000-1-2-research0003 · Cited by 547
Self-citation
Research works printed but not linked: 21
- Breiman, 1996, Heuristics of instability and stabilization in model selection, Ann. Statist., 24, 2350, 10.1214/aos/1032181158 · DOI
- Dettling, 2004, Finding predictive gene groups from microarray data, J. Multiv. Anal., 90, 106, 10.1016/j.jmva.2004.02.012 · DOI
- Díaz-Uriarte, 2003, Tech-nical Report
- Donoho, 1995, Wavelet shrinkage: asymptopia (with discussion)?, J. R. Statist. Soc., 57, 301, 10.1111/j.2517-6161.1995.tb02032.x · DOI
- Efron, 2004, Least angle regression, Ann. Statist., 32, 407, 10.1214/009053604000000067 · DOI · Paper page
- Fan, 2001, Variable selection via nonconcave penalized likelihood and its oracle properties, J. Am. Statist. Ass., 96, 1348, 10.1198/016214501753382273 · DOI
- Frank, 1993, A statistical view of some chemometrics regression tools, Technometrics, 35, 109, 10.1080/00401706.1993.10485033 · DOI
- Friedman, 1989, Regularized discriminant analysis, J. Am. Statist. Ass., 84, 249, 10.1080/01621459.1989.10478752 · DOI
- Friedman, 2004, Discussion of boosting papers, Ann. Statist., 32, 102
- Fu, 1998, Penalized regression: the bridge versus the lasso, J. Computnl Graph. Statist., 7, 397, 10.1080/10618600.1998.10474784 · DOI
- Golub, 1999, Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science, 286, 513, 10.1126/science.286.5439.531 · DOI · Paper page
- Hastie, 2003, Supervised harvesting of expression trees, Genome Biol., 2, 0003.1
- Hoerl, 1988, Encyclopedia of Statistical Sciences, 129
- Rosset, 2004, Boosting as a regularized path to a maximum margin classifier, J. Mach. Learn. Res., 5, 941
- Segal, 2003, Regression approach for microarray data analysis, J. Computnl Biol., 10, 961, 10.1089/106652703322756177 · DOI
- Stamey, 1989, Prostate specific antigen in the diagnosis and treatment of adenocarcinoma of the prostate ii: radical prostatectomy treated patients, J. Urol., 16, 1076, 10.1016/S0022-5347(17)41175-X · DOI
- Tibshirani, 2002, Diagnosis of multiple cancer types by shrunken centroids of gene expression, Proc. Natn. Acad. Sci. USA, 99, 6567, 10.1073/pnas.082099299 · DOI
- Tusher, 2001, Significance analysis of microarrays applied to transcriptional responses to ionizing radiation, Proc. Natn. Acad. Sci. USA, 98, 5116, 10.1073/pnas.091062498 · DOI · Paper page
- West, 2001, Predicting the clinical status of human breast cancer using gene expression profiles, Proc. Natn. Acad. Sci. USA, 98, 11462, 10.1073/pnas.201162998 · DOI
- Zhang, 2004, Statistical behavior and consistency of classification methods based on convex risk minimization, Ann. Statist., 32, 469, 10.1214/aos/1079120130 · DOI
- Zhu, 2004, Classification of gene microarrays by penalized logistic regression, Biostatistics, 5, 427, 10.1093/biostatistics/kxg046 · DOI
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Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aace8c · Cited by 5,115
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Metadata
- Authors
- Zou, H., & Hastie, T.
- Affiliations
- Stanford University (US)
- Corresponding author
- 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
- 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
- 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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