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Activity Number: 368
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #321018 View Presentation
Title: A Spatial Shrinkage Prior for Sparse Signal Detection
Author(s): An-Ting Jhuang* and Brian J. Reich and Montse Fuentes
Companies: North Carolina State University and North Carolina State University and North Carolina State University

Sparse signal detection in imaging data has been widely studied. However, there are remaining uncertainties and methodological challenges in relaxing the assumption of identical and independent parameters. We propose a spatial shrinkage prior motivated by the horseshoe estimator. The introduced prior allows correlation among parameters, retains characteristics of the horseshoe prior, and avoids computational difficulties encountered in discrete mixture models. We demonstrate the theoretical properties including concentration towards zero, tail dependence, and posterior behaviors. We conduct a simulation study to evaluate the properties of the proposed prior under different settings. We apply our method to X-ray diffraction images to detect pattern changes and we validate the performance of the proposed approach in relation to competitors.

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

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