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Modélisation des Co-Expositions aux Pesticides : une Approche Bayésienne Nonparamétrique

Abstract : This work introduces a specific application of the Bayesian nonparametric methodology in the food risk analysis framework. The goal is to determine mixture of pesticides residues which are simultaneously present in the diet, to give directions for future toxicological experiments for studying possible combined effects of those mixtures. Namely, the joint distribution of the exposures to a large number of pesticides is assessed from the available consumption data and contamination analyses. We propose to model the co-exposures by a Dirichlet process mixture based on a multivariate Gaussian kernel so as to determine clusters of pesticides jointly present in the diet at high doses. The posterior distributions and the optimal partition are computed through a Gibbs sampler based on stick-breaking priors. To reduce computational time due to the high dimensional data, a random block sampling is used. Finally, the clustering among individuals also obtained as an auxiliary output of these analyses is discussed in a risk management perspective.
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https://hal.inria.fr/inria-00386735
Contributor : Conférence Jds2009 <>
Submitted on : Friday, May 22, 2009 - 9:17:05 AM
Last modification on : Tuesday, June 2, 2020 - 8:58:02 PM
Long-term archiving on: : Thursday, June 10, 2010 - 11:40:22 PM

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  • HAL Id : inria-00386735, version 1

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Amélie Crépet, Jessica Tressou. Modélisation des Co-Expositions aux Pesticides : une Approche Bayésienne Nonparamétrique. 41èmes Journées de Statistique, SFdS, Bordeaux, 2009, Bordeaux, France, France. ⟨inria-00386735⟩

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