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Activity Number:
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580
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Type:
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Invited
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Date/Time:
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Thursday, August 6, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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ASA Special Interest Group on Statistical Learning and Data Mining
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| Abstract - #303288 |
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Title:
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Least Angle and L1 Penalized Regression: A Review
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Author(s):
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Tim Hesterberg*+ and Nam Hee Choi and Lukas Meier and Chris Fraley
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Companies:
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Google, Inc. and University of Michigan and ETH and Insilicos
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Address:
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651 N 34th St, Seattle, WA, 98103,
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Keywords:
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lasso ; regression ; regularization ; L1 penalty ; variable selection
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Abstract:
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Least angle regression is a promising technique for variable selection applications, offering a nice alternative to stepwise regression. It provides an explanation for the similar behavior of LASSO (L1-penalized regression) and forward stagewise regression, and provides a fast implementation of both. The idea has caught on rapidly, and sparked a great deal of research interest. We'll give an overview of Least Angle Regression and the current state of related research.
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