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Gaussian graphical modeling for spectrometric data analysis

Citation Link: https://doi.org/10.15480/882.4876
Publikationstyp
Journal Article
Date Issued
2022-01-06
Sprache
English
Author(s)
Codazzi, Laura  
Colombi, Alessandro  
Gianella, Matteo  
Argiento, Raffaele  
Paci, Lucia  
Pini, Alessia  
Institut
Algorithmen und Komplexität E-11  
TORE-DOI
10.15480/882.4876
TORE-URI
http://hdl.handle.net/11420/11674
Journal
Computational statistics & data analysis  
Volume
174
Article Number
107416
Citation
Computational Statistics and Data Analysis (2022)
Publisher DOI
10.1016/j.csda.2021.107416
Scopus ID
2-s2.0-85123107236
Publisher
Elsevier Science
Peer Reviewed
true
Motivated by the analysis of spectrometric data, a Gaussian graphical model for learning the dependence structure among frequency bands of the infrared absorbance spectrum is introduced. The spectra are modeled as continuous functional data through a B-spline basis expansion and a Gaussian graphical model is assumed as a prior specification for the smoothing coefficients to induce sparsity in their precision matrix. Bayesian inference is carried out to simultaneously smooth the curves and to estimate the conditional independence structure between portions of the functional domain. The proposed model is applied to the analysis of infrared absorbance spectra of strawberry purees.
Subjects
Bayesian inference
Birth-death process
Functional data analysis
Model selection
Spectrum analysis
DDC Class
004: Informatik
510: Mathematik
Funding Organisations
Università Cattolica del Sacro Cuore, Italy
More Funding Information
Research supported by sharing.city.college, organized by ahoi.digital, and funded by the Ministry of Science, Research, Equalities and Districts of the Free and Hanseatic City of Hamburg. Funding: the research of the fourth, fifth and sixth author has been partially supported by a grant from Università Cattolica del Sacro Cuore, Italy (track D1).
Publication version
submittedVersion
Lizenz
http://rightsstatements.org/vocab/InC/1.0/
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