Bayesian Analysis

Bayesian nonparametric model with clustering individual co-exposure to pesticides found in the French diet

Amélie Crépet and Jessica Tressou

Full-text: Open access

Abstract

This work introduces a specific application of Bayesian nonparametric statistics to the food risk analysis framework. The goal was to determine the cocktails of pesticide residues to which the French population is simultaneously exposed through its current diet in order to study their possible combined effects on health through toxicological experiments. To do this, the joint distribution of exposures to a large number of pesticides, which we called the co-exposure distribution, was assessed from the available consumption data and food contamination analyses. We propose modelling the co-exposure using a Dirichlet process mixture based on a multivariate Gaussian kernel so as to determine groups of individuals with similar co-exposure patterns. Posterior distributions and optimal partition were computed through a Gibbs sampler based on stick-breaking priors. The study of the correlation matrix of the sub-population co-exposures will be used to define the cocktails of pesticides to which they are jointly exposed at high doses. To reduce the computational burden due to the high data dimensionality, a random-block sampling approach was used. In addition, we propose to account for the uncertainty of food contamination through the introduction of an additional level of hierarchy in the model. The results of both specifications are described and compared.

Article information

Source
Bayesian Anal. Volume 6, Number 1 (2011), 127-144.

Dates
First available in Project Euclid: 13 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1339611943

Digital Object Identifier
doi:10.1214/11-BA604

Mathematical Reviews number (MathSciNet)
MR2781810

Zentralblatt MATH identifier
1330.62415

Subjects
Primary: 62P12: Applications to environmental and related topics
Secondary: 60G57: Random measures 62F15: Bayesian inference 62G99: None of the above, but in this section 62H25: Factor analysis and principal components; correspondence analysis

Keywords
Dirichlet process Bayesian nonparametric modeling Multivariate Normal mixtures Clustering Multivariate exposure Food risk analysis

Citation

Crépet, Amélie; Tressou, Jessica. Bayesian nonparametric model with clustering individual co-exposure to pesticides found in the French diet. Bayesian Anal. 6 (2011), no. 1, 127--144. doi:10.1214/11-BA604. https://projecteuclid.org/euclid.ba/1339611943


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