國立東華大學應用數學系

專    題    演    講

主講人：陳瑞彬
Department of Statistics, National Cheng Kung University

講  題：Bayesian Sparse Group Selection

時  間：102 年 03 月 08 日 (星期五)  15:20-16:50

地  點：理工一館 A324 會議室

摘   要

    This article proposes a Bayesian approach for the sparse group selection problem 
in regression models. In this problem, the variables or regressors are partitioned into
 different groups. It is assumed that only a small number of groups are active or important 
for explaining the response variable. 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
 variable, indicating whether the variable is active or not. In this Bayesian formulation, the
 sparse group selection problem can be solved by sampling from the posterior distribution 
of the two layers of indicator variables as well as the coefficients of the active variables. 
We adopt a group-wise Gibbs sampler for posterior sampling. We demonstrate the proposed
 method by simulation studies as well as a real example. The simulation results show that 
the proposed method is competitive with 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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