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Activity Number: 552
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #321344 View Presentation
Title: Scalable Bayesian Variable Selection for Structured Data
Author(s): Suprateek Kundu* and Changgee Chang and Qi Long
Companies: Emory University and Emory University and Emory University
Keywords: adaptive lasso ; Bayesian shrinkage ; EM algorithm ; oracle property ; selection consistency ; structured variable selection

Variable selection for structured covariates lying on an underlying known graph is a problem motivated by practical applications, and has been a topic of increasing interest, with the primary focus being on spike and slab type approaches. However, most of the existing methods are not scalable to high dimensional settings, for example, in genomic studies involving tens of thousands of genes lying on known pathways. We propose a Bayesian shrinkage approach which incorporates prior information by smoothing the shrinkage parameters, with the coefficients for two connected variables in the graph being encouraged to have a similar degree of shrinkage. We fit our model via a computationally efficient expectation maximization algorithm which is scalable to high dimensional settings (p =100000). Theoretical properties for fixed as well as increasing dimensions are established, even when the number of variables increases faster than the sample size. We demonstrate the advantages of our approach in terms of accuracy, computational speed, and scalability, via a simulation study, and apply the method to a real data example.

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

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