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Title: Spatial Signal Detection Using Continuous Shrinkage Priors

Abstract

Motivated by the problem of detecting changes in two-dimensional X-ray diffraction data, we propose a Bayesian spatial model for sparse signal detection in image data. Our model places considerable mass near zero and has heavy tails to reflect the prior belief that the image signal is zero for most pixels and large for an important subset. We show that the spatial prior places mass on nearby locations simultaneously being zero, and also allows for nearby locations to simultaneously be large signals. The form of the prior also facilitates efficient computing for large images. We conduct a simulation study to evaluate the properties of the proposed prior and show that it outperforms other spatial models. As a result, we apply our method in the analysis of X-ray diffraction data from a two-dimensional area detector to detect changes in the pattern when the material is exposed to an electric field.

Authors:
 [1];  [2];  [1];  [1]; ORCiD logo [3];  [1];  [1]
  1. North Carolina State Univ., Raleigh, NC (United States)
  2. Virginia Commonwealth Univ., Richmond, VA (United States)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1504020
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Technometrics
Additional Journal Information:
Journal Volume: 61; Journal Issue: 4; Journal ID: ISSN 0040-1706
Publisher:
Taylor & Francis
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Bayesian variable selection; High-dimensional data; Image analysis; X-ray diffraction

Citation Formats

Jhuang, An -Ting, Fuentes, Montserrat, Jones, Jacob L., Esteves, Giovanni, Fancher, Chris M., Furman, Marschall, and Reich, Brian J. Spatial Signal Detection Using Continuous Shrinkage Priors. United States: N. p., 2019. Web. doi:10.1080/00401706.2018.1546622.
Jhuang, An -Ting, Fuentes, Montserrat, Jones, Jacob L., Esteves, Giovanni, Fancher, Chris M., Furman, Marschall, & Reich, Brian J. Spatial Signal Detection Using Continuous Shrinkage Priors. United States. https://doi.org/10.1080/00401706.2018.1546622
Jhuang, An -Ting, Fuentes, Montserrat, Jones, Jacob L., Esteves, Giovanni, Fancher, Chris M., Furman, Marschall, and Reich, Brian J. Fri . "Spatial Signal Detection Using Continuous Shrinkage Priors". United States. https://doi.org/10.1080/00401706.2018.1546622. https://www.osti.gov/servlets/purl/1504020.
@article{osti_1504020,
title = {Spatial Signal Detection Using Continuous Shrinkage Priors},
author = {Jhuang, An -Ting and Fuentes, Montserrat and Jones, Jacob L. and Esteves, Giovanni and Fancher, Chris M. and Furman, Marschall and Reich, Brian J.},
abstractNote = {Motivated by the problem of detecting changes in two-dimensional X-ray diffraction data, we propose a Bayesian spatial model for sparse signal detection in image data. Our model places considerable mass near zero and has heavy tails to reflect the prior belief that the image signal is zero for most pixels and large for an important subset. We show that the spatial prior places mass on nearby locations simultaneously being zero, and also allows for nearby locations to simultaneously be large signals. The form of the prior also facilitates efficient computing for large images. We conduct a simulation study to evaluate the properties of the proposed prior and show that it outperforms other spatial models. As a result, we apply our method in the analysis of X-ray diffraction data from a two-dimensional area detector to detect changes in the pattern when the material is exposed to an electric field.},
doi = {10.1080/00401706.2018.1546622},
journal = {Technometrics},
number = 4,
volume = 61,
place = {United States},
year = {2019},
month = {3}
}

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