Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Détails

ID Serval
serval:BIB_1A5FA4F77E3D
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.
Périodique
Annals of Occupational Hygiene
Auteur(s)
Lavoué Jérôme, Droz Pierre-Olivier
ISSN
1475-3162[electronic], 0003-4878[linking]
Statut éditorial
Publié
Date de publication
2009
Peer-reviewed
Oui
Volume
53
Numéro
2
Pages
173-180
Langue
anglais
Résumé
Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.
Mots-clé
Environmental Monitoring/statistics & numerical data, Humans, Linear Models, Occupational Exposure/analysis, Occupational Exposure/statistics & numerical data, Occupational Health/statistics & numerical data
Pubmed
Web of science
Création de la notice
22/02/2011 17:34
Dernière modification de la notice
03/03/2018 14:28
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