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Activity Number: 256 - Contributed Poster Presentations: Section on Bayesian Statistical Science
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #330816
Title: Bayesian Sparse Regression in the Presence of Nuisance Parameters
Author(s): Seonghyun Jeong* and Subhashis Ghoshal
Companies: North Carolina State University and North Carolina State Univeristy
Keywords: Adaptive contraction rates; High-dimensional regression; Sparse prior

We study Bayesian procedures for high-dimensional sparse regression when unknown nuisance parameters are involved. A mixture of point masses at zero and continuous distributions is used for the prior distribution on sparse regression coefficients, and appropriate prior distributions are used for nuisance parameters to yield the optimal convergence rates of sparse regression coefficients and nuisance parameters. It is shown that the procedure yields strong model selection consistency. A Bernstein-von Mises-type theorem for sparse regression coefficients is also obtained for uncertainty quantification.

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

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