Brown, P.J. and Vannucci, M. and Fearn, T. (1998) Bayesian wavelength selection in multicomponent analysis. Journal of Chemometrics, 12 (3). pp. 173-82. ISSN 0886-9383.
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Multicomponent analysis attempts to simultaneously predict the ingredients of a mixture. If near-infrared spectroscopy provides the predictor variables, then modern scanning instruments may offer absorbances at a very large number of wavelengths. Although it is perfectly possible to use whole spectrum methods (e.g. PLS, ridge and principal component regression), for a number of reasons it is often desirable to select a small number of wavelengths from which to construct the prediction equation relating absorbances to composition. This paper considers wavelength selection with a view to using the chosen wavelengths to simultaneously predict the compositional ingredients and is therefore an example of multivariate variable selection. It adopts a binary exclusion/inclusion latent variable formulation of selection and uses a Bayesian approach. Problems of search of the vast number of possible selected models are overcome by a Markov chain Monte Carlo sampling technique.
|Uncontrolled keywords:||multivariate regression; Bayesian wavelength selection; Markov chain Monte Carlo (MCMC); Metropolis algorithm; NIR spectroscopy; multicomponent analysis; selection bias; model averaging|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Q Science > QD Chemistry
Q Science > QA Mathematics (inc Computing science)
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science|
|Depositing User:||I. Ghose|
|Date Deposited:||05 Apr 2009 14:17|
|Last Modified:||05 Apr 2009 14:17|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/17608 (The current URI for this page, for reference purposes)|
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