Abstract Details
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Activity Number:
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33
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Type:
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Contributed
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Date/Time:
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Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #309661 |
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Title:
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Generalized Least Angle Regression
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Author(s):
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George Terrell*+
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Companies:
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VA Poly. Inst. & State Univ.
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Keywords:
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linear regression ;
model selection ;
LASSO
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Abstract:
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Least-angle regression (LARS) is an algorithm that regularizes models by simultaneously selecting variables and shrinking predictions. It characterizes possible models as those for which the residual information about the dependent variable is equal for all active independent variables. We show that this algorithm may be generalized to regression problems for which the criterion of good fit (such as the log-likelihood) is twice differentiable and convex.
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Authors who are presenting talks have a * after their name.
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