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Activity Number:
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506
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Type:
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Contributed
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Date/Time:
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Thursday, August 10, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #306150 |
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Title:
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The Bayesian Lasso
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Author(s):
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Trevor Park*+ and George Casella
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Companies:
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University of Florida and University of Florida
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Address:
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Department of Statistics, Gainesville, FL, 32611,
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Keywords:
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Gibbs sampler ; inverse Gaussian
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Abstract:
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The Lasso estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the priors on the regression parameters are independent double-exponential (Laplace) distributions. This posterior can also be accessed through a Gibbs sampler using conjugate normal priors for the regression parameters, with independent exponential hyperpriors on their variances. This leads to tractable full conditional distributions through a connection with the inverse Gaussian distribution. Although the Bayesian Lasso does not automatically perform variable selection, it does provide standard errors and Bayesian credible intervals. Moreover, the structure of the hierarchical model provides both Bayesian and likelihood methods for selecting the Lasso parameter.
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- Authors who are presenting talks have a * after their name.
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