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.

(2022). Gaussian graphical modeling for spectrometric data analysis [journal article - articolo]. In COMPUTATIONAL STATISTICS & DATA ANALYSIS. Retrieved from http://hdl.handle.net/10446/227953

Gaussian graphical modeling for spectrometric data analysis

Argiento, Raffaele;
2022

Abstract

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.
articolo
Codazzi, Laura; Colombi, Alessandro; Gianella, Matteo; Argiento, Raffaele; Paci, Lucia; Pini, Alessia
(2022). Gaussian graphical modeling for spectrometric data analysis [journal article - articolo]. In COMPUTATIONAL STATISTICS & DATA ANALYSIS. Retrieved from http://hdl.handle.net/10446/227953
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10446/227953
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