Regularization and variable selection via the elastic net

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...

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Veröffentlicht in:Journal of the Royal Statistical Society. Series B, Statistical methodology Jg. 67; H. 2; S. 301 - 320
Hauptverfasser: Zou, Hui, Hastie, Trevor
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford, UK Blackwell Publishing Ltd 01.04.2005
Blackwell Publishers
Blackwell
Royal Statistical Society
Oxford University Press
Schriftenreihe:Journal of the Royal Statistical Society Series B
Schlagworte:
Algorithms
Coefficients
Correlation
Datasets
Elasticity
Exact sciences and technology
Feature selection
Forecasts
Genes
Grouping effect
LARS algorithm
Lasso
Leukemia
Mathematics
Modeling
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Penalization
Performance prediction
Prostate cancer
p≫n problem
Regression coefficients
Sciences and techniques of general use
Simulation
Statistical methods
Statistics
Studies
Variable coefficients
Variable selection
Regularization method
Statistical method
Penalization
Correlation analysis
Grouping effect
Lasso
p >> n problem
Elastic net
Computing
Statistical simulation
LARS algorithm
Variable selection
ISSN:1369-7412, 1467-9868
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
Bibliographie:ark:/67375/WNG-KH669V8K-B
istex:D97691B294E9C48354AC7E3548802EDD7A293D63
ArticleID:RSSB503
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ObjectType-Article-2
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ISSN:1369-7412
1467-9868
DOI:10.1111/j.1467-9868.2005.00503.x