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Faculty Candidate Seminar: Regularization and Variable Selection via the Elastic Net
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Regularization and Variable Selection via the Elastic Net
Hui Zou
Department of Statistics
Stanford University
In the practice of statistical modeling, it is often desirable to have an accurate predictive model with a sparse representation. The lasso is a promising model building technique, performing continuous shrinkage and variable selection simultaneously. Although the lasso has shown success in many situations, it may produce unsatisfactory results in some scenarios: (1) the number of predictors (greatly) exceeds the number of observations; (2) the predictors are highly correlated and form
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- Workflow status: Published
- Created by: Barbara Christopher
- Created: 10/08/2010
- Modified By: Darin Givens
- Modified: 04/24/2026
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