Risk assessment of dietary exposure to pesticides using a Bayesian method

M.J. Paulo; H. van der Voet; M.J.W. Jansen; C.J.F. ter Braak; J.D. van Klaveren

doi:10.1002/ps.1060

Risk assessment of dietary exposure to pesticides using a Bayesian method

M.J. Paulo, H. van der Voet, M.J.W. Jansen, C.J.F. ter Braak, J.D. van Klaveren

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Abstract

Risk assessment of pesticides can be a statistically difficult problem because pesticides occur only occasionally, but they may occur on multiple components in the diet. A Bayesian statistical model is presented which incorporates multivariate modelling of food consumption and modelling of pesticide measurements which are for a large part below a measurement threshold. It is shown that Bayesian modelling is feasible for a limited number of food components, and that in a data-rich situation the model compares well with an empirical Monte Carlo modelling

Original language	English
Pages (from-to)	759-766
Journal	Pest Management Science
Volume	61
Issue number	8
DOIs	https://doi.org/10.1002/ps.1060
Publication status	Published - 2005

Keywords

food chemicals
validation
regression
models

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10.1002/ps.1060

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title = "Risk assessment of dietary exposure to pesticides using a Bayesian method",

abstract = "Risk assessment of pesticides can be a statistically difficult problem because pesticides occur only occasionally, but they may occur on multiple components in the diet. A Bayesian statistical model is presented which incorporates multivariate modelling of food consumption and modelling of pesticide measurements which are for a large part below a measurement threshold. It is shown that Bayesian modelling is feasible for a limited number of food components, and that in a data-rich situation the model compares well with an empirical Monte Carlo modelling",

keywords = "food chemicals, validation, regression, models",

author = "M.J. Paulo and {van der Voet}, H. and M.J.W. Jansen and {ter Braak}, C.J.F. and {van Klaveren}, J.D.",

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journal = "Pest Management Science",

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AU - Paulo, M.J.

AU - van der Voet, H.

AU - Jansen, M.J.W.

AU - ter Braak, C.J.F.

AU - van Klaveren, J.D.

PY - 2005

Y1 - 2005

N2 - Risk assessment of pesticides can be a statistically difficult problem because pesticides occur only occasionally, but they may occur on multiple components in the diet. A Bayesian statistical model is presented which incorporates multivariate modelling of food consumption and modelling of pesticide measurements which are for a large part below a measurement threshold. It is shown that Bayesian modelling is feasible for a limited number of food components, and that in a data-rich situation the model compares well with an empirical Monte Carlo modelling

AB - Risk assessment of pesticides can be a statistically difficult problem because pesticides occur only occasionally, but they may occur on multiple components in the diet. A Bayesian statistical model is presented which incorporates multivariate modelling of food consumption and modelling of pesticide measurements which are for a large part below a measurement threshold. It is shown that Bayesian modelling is feasible for a limited number of food components, and that in a data-rich situation the model compares well with an empirical Monte Carlo modelling

KW - food chemicals

KW - validation

KW - regression

KW - models

U2 - 10.1002/ps.1060

DO - 10.1002/ps.1060

M3 - Article

SN - 1526-498X

VL - 61

SP - 759

EP - 766

JO - Pest Management Science

JF - Pest Management Science

IS - 8

ER -

Risk assessment of dietary exposure to pesticides using a Bayesian method

Abstract

Keywords

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