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Activity Number: 432
Type: Invited
Date/Time: Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #310773
Title: Hierarchical Sparsity Priors for Regression Models
Author(s): Jim Griffin*+ and Phil Brown
Companies: University of Kent and University of Kent
Keywords: Bayesian regularization ; Structured priors ; Generalized additive models ; Interactions ; Normal-gamma priors

Sparse regression methods, which assume that a subset of effects are negligible, have become increasingly important. This paper describes the construction of hierarchical prior distributions in sparse regression problems. These allow dependence between the regression coefficients and the shrinkage of different regression coefficent to zero to related. The properties of the prior are discussed and applications to linear models with interactions and generalized additive models are used as illustrations.

Authors who are presenting talks have a * after their name.

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