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2017 Structured regression models for high-dimensional spatial spectroscopy data
Arash A. Amini, Elizaveta Levina, Kerby A. Shedden
Electron. J. Statist. 11(2): 4151-4178 (2017). DOI: 10.1214/17-EJS1301


Modeling and analysis of spectroscopy data is an active area of research with applications to chemistry and biology. This paper focuses on modelling high-dimensional spectra for the purpose of noise reduction and prediction in problems where the spectra can be used as covariates. We propose a functional representation of the spectra as well as functional regression model that accommodates multiple spatial dimensions. Both steps emphasize sparsity to reduce the number of parameters and mitigate over-fitting. The motivating application for these models, discussed in some detail, is predicting bone-mineral-density (BMD), an important indicator of fracture healing, from Raman spectra, in both the in vivo and ex vivo settings of a bone fracture healing experiment. To illustrate the general applicability of the method, we also use it to predict lipoprotein concentrations from spectra obtained by nuclear magnetic resonance (NMR) spectroscopy.


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Arash A. Amini. Elizaveta Levina. Kerby A. Shedden. "Structured regression models for high-dimensional spatial spectroscopy data." Electron. J. Statist. 11 (2) 4151 - 4178, 2017.


Received: 1 September 2016; Published: 2017
First available in Project Euclid: 25 October 2017

zbMATH: 1380.62248
MathSciNet: MR3715824
Digital Object Identifier: 10.1214/17-EJS1301

Keywords: functional data , spatial data , spectroscopy , structured regression


Vol.11 • No. 2 • 2017
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