Spatial Signal Detection Using Continuous Shrinkage Priors

An Ting Jhuang, Montserrat Fuentes, Jacob L. Jones, Giovanni Esteves, Chris M. Fancher, Marschall Furman, Brian J. Reich

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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. 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.

Original languageEnglish
Pages (from-to)494-506
Number of pages13
JournalTechnometrics
Volume61
Issue number4
DOIs
StatePublished - Oct 2 2019

Funding

The authors thank the editor, associate editor, and two anonymous referees for insightful and constructive comments on an earlier version of the manuscript. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

FundersFunder number
DOE Public Access Plan
LLCDE-AC05-00OR22725
UT-Battelle
United States Government
U.S. Department of Energy

    Keywords

    • Bayesian variable selection
    • High-dimensional data
    • Image analysis
    • X-ray diffraction

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