Abstract Details
Activity Number:
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191
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #309686 |
Title:
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Generalized Elastic Net Regression
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Author(s):
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Geoffroy Mouret*+ and Jean-Jules Brault and Vahid Partovi Nia
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Companies:
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Ecole Polytechnique de Montreal and Ecole Polytechnique de Montreal and École Polytechnique Montréal
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Keywords:
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elastic net ;
fitted values ;
generalized lasso ;
least angle regression ;
variable selection
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Abstract:
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This work presents a variation of the elastic net penalization method. We propose applying a combined l1 and l2 norm penalization on a linear combination of regression parameters. This approach is an alternative to the l1-penalization for variable selection, but takes care of the correlation between the linear combination of parameters. We devise a path algorithm fitting method similar to the one proposed for the least angle regression. Furthermore, a one-shot estimation technique of l2 regularization parameter is proposed as an alternative to cross-validation. A simulation study is conducted to check the validity of the new technique.
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Authors who are presenting talks have a * after their name.
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