Statistics Seminar - Ray-Bing Chen

Event Details
  • Date/Time:
    • Thursday September 3, 2015
      11:00 am
  • Location: Advisory Board Room, GC 402
  • Phone:
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  • Fee(s):
    N/A
  • Extras:
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Summaries

Summary Sentence: Statistics Seminar - Ray-Bing Chen

Full Summary: No summary paragraph submitted.

TITLE:  Bayesian Sparse Group Selection

ABSTRACT:

A Bayesian approach is proposed 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.

Additional Information

In Campus Calendar
No
Groups

H. Milton Stewart School of Industrial and Systems Engineering (ISYE)

Invited Audience
Undergraduate students, Faculty/Staff, Graduate students
Categories
Seminar/Lecture/Colloquium
Keywords
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Status
  • Created By: Anita Race
  • Workflow Status: Published
  • Created On: Aug 27, 2015 - 10:56am
  • Last Updated: Apr 13, 2017 - 5:18pm