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Bayesian linear regression and variable selection for spectroscopic calibration.

Chen, T and Martin, E (2009) Bayesian linear regression and variable selection for spectroscopic calibration. Anal Chim Acta, 631 (1). 13 - 21. ISSN 0003-2670

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Abstract

This paper presents a Bayesian approach to the development of spectroscopic calibration models. By formulating the linear regression in a probabilistic framework, a Bayesian linear regression model is derived, and a specific optimization method, i.e. Bayesian evidence approximation, is utilized to estimate the model "hyper-parameters". The relation of the proposed approach to the calibration models in the literature is discussed, including ridge regression and Gaussian process model. The Bayesian model may be modified for the calibration of multivariate response variables. Furthermore, a variable selection strategy is implemented within the Bayesian framework, the motivation being that the predictive performance may be improved by selecting a subset of the most informative spectral variables. The Bayesian calibration models are applied to two spectroscopic data sets, and they demonstrate improved prediction results in comparison with the benchmark method of partial least squares.

Item Type: Article
Additional Information: NOTICE: this is the author’s version of a work that was accepted for publication in Analytica Chimica Acta. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Analytica Chimica Acta, 631 (1), January 2009, DOI 10.1016/j.aca.2008.10.014.
Divisions: Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Depositing User: Symplectic Elements
Date Deposited: 27 Jan 2012 14:52
Last Modified: 23 Sep 2013 18:57
URI: http://epubs.surrey.ac.uk/id/eprint/69206

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