Home > Events > 2015 Seminars & Colloquia > Ray-Bing Chen, Department of Statistics, National Cheng-Kung University, Taiwan

Ray-Bing Chen, Department of Statistics, National Cheng-Kung University, Taiwan

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Bayesian Sparse Group Selection
When
03 December 2015 from 10:00 AM to 11:30 AM
Where
327 Thomas Building
Contact Name
Contact Phone
867-6326
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This article proposes a Bayesian approach for the sparse group selection
problem in the regression model. In this problem, the variables are
partitioned into different disjoint groups. It is assumed that only a small
number of groups are active for explaining the response variable, and it is
further assumed that within each active group only a small number of
variables are active. We adopt a Bayesian hierarchical formulation, where
each candidate group is associated with a binary variable indicating
whether the group is active or not. Within each group, each candidate
variable is also associated with a binary indicator, too. Thus the sparse
group selection problem can be solved by sampling from the posterior
distribution of the two layers of indicator variables. We adopt a
group-wise Gibbs sampler for posterior sampling. We demonstrate the
proposed method by simulation studies as well as real examples. The
simulation results show that the proposed method performs better than the
sparse group Lasso in terms of selecting the active groups as well as
identifying the active variables within the selected groups.

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