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Spatial Signal Detection Using Continuous Shrinkage Priors
Journal article   Peer reviewed

Spatial Signal Detection Using Continuous Shrinkage Priors

An-Ting Jhuang, Montserrat Fuentes, Jacob L Jones, Giovanni Esteves, Chris M Fancher, Marschall Furman and Brian J Reich
Technometrics, Vol.61(4), pp.494-506
0
03/22/2019
DOI: 10.1080/00401706.2018.1546622
PMCID: PMC6853616
PMID: 31723308
url
https://figshare.com/articles/dataset/Spatial_Signal_Detection_Using_Continuous_Shrinkage_Priors/7491635View
Open Access

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

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