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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Official URL http://dx.doi.org/10.1002/(SICI)1099-128X(199805/0... |

## Abstract

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.

Item Type: | Article |
---|---|

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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