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Abstract Details
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Activity Number:
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568
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #305511 |
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Title:
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Groupwise Elastic Net
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Author(s):
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Miae Oh*+ and Yongdai Kim
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Companies:
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and Seoul National University
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Address:
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College of Natural Sciences, Seoul Natio, Seoul, _, 151-742, South Korea
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Keywords:
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Elastic net ;
group variable selection ;
high-dimensional data ;
penalized regression
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Abstract:
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In this talk, we consider a problem of model selection and estimation in sparse, high-dimensional regression models where covariates are grouped. We propose a new regularization method which can reflected a correlation structure between groups. We propose a combination of doubly sparse and groupwise quadratic penalties where the former ensures groupwise sparsity and the later promotes simultaneous selection of highly correlated groups. The proposed method is applied to real examples.
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