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Activity Number:
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395
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Type:
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Invited
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Date/Time:
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Wednesday, August 5, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #302742 |
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Title:
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The Horseshoe Approach to Shrinkage
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Author(s):
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Carlos M. Carvalho*+ and Nicholas Polson and James Scott
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Companies:
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The University of Chicago and The University of Chicago and Duke University
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Address:
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Graduate School of Business, Chicago, IL, 60637,
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Keywords:
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Shrinkage ; Sparsity ; Bayesian Lasso ; Default priors ; Model selection
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Abstract:
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In this talk, I will present a new approach to sparse-signal detection called the horseshoe estimator. The horseshoe is a close cousin of the lasso in that it arises from the same class of multivariate scale mixtures of normals, but that it is more robust alternative at handling unknown sparsity patterns. A theoretical framework is proposed for understanding why the horseshoe is a better default sparsity estimator than those that arise from powered-exponential priors. Comprehensive numerical evidence is presented to show that the difference in performance can often be large. Most importantly, I will show that the horseshoe estimator corresponds quite closely to the answers one would get if one pursued a full Bayesian model-averaging approach using a point mass at zero for noise, and a continuous density for signals.
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