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Activity Number: 297 - SBSS Student Travel Award Session 1
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #328539 Presentation
Title: Bayesian Regularization for Graphical Models with Unequal Shrinkage
Author(s): Lingrui Gan* and Naveen Naidu Narisetty and Feng Liang
Companies: University of Illinois At Urbana-Champaign and University of Illinois at Urbana Champaign and University of Illinois at Urbana-Champaign
Keywords: precision matrix estimation; sparse Gaussian graphical model; spike-and-slab priors; Bayesian regularization

We consider a Bayesian framework for estimating a high-dimensional sparse precision matrix, in which adaptive shrinkage and sparsity are induced by a mixture of Laplace priors. Besides discussing our formulation from the Bayesian standpoint, we investigate the MAP (maximum a posteriori) estimator from a penalized likelihood perspective that gives rise to a new non-convex penalty approximating the \ell_0 penalty. Optimal error rates for estimation consistency in terms of various matrix norms along with selection consistency for sparse structure recovery are shown for the unique MAP estimator under mild conditions. For fast and efficient computation, an EM algorithm is proposed to compute the MAP estimator of the precision matrix and (approximate) posterior probabilities on the edges of the underlying sparse structure. Through extensive simulation studies and a real application to a call center data, we have demonstrated the fine performance of our method compared with existing alternatives.

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

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