Using Empirical Likelihood to Combine Data: Application to Food Risk Assessment

Authors

  • Amélie Crépet,

    1. INRA, UR1204, Mét@risk, AgroParisTech, 16 rue Claude Bernard, F75231 Paris, France
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  • Hugo Harari-Kermadec,

    1. INRA, UR1001, CORELA, 65 bd de Brandebourg, F94205 Ivry-sur-Seine, France
    2. CREST-LS, 3 Avenue Pierre Larousse, F92245 Malakoff, France
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  • Jessica Tressou

    Corresponding author
    1. INRA, UR1204, Mét@risk, AgroParisTech, 16 rue Claude Bernard, F75231 Paris, France
    2. HKUST-ISMT, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
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e-mail: jessica.tressou@agroparistech.fr

Abstract

Summary This article introduces an original methodology based on empirical likelihood, which aims at combining different food contamination and consumption surveys to provide risk managers with a risk measure, taking into account all the available information. This risk index is defined as the probability that exposure to a contaminant exceeds a safe dose. It is naturally expressed as a nonlinear functional of the different consumption and contamination distributions, more precisely as a generalized U-statistic. This nonlinearity and the huge size of the data sets make direct computation of the problem unfeasible. Using linearization techniques and incomplete versions of the U-statistic, a tractable “approximated” empirical likelihood program is solved yielding asymptotic confidence intervals for the risk index. An alternative “Euclidean likelihood program” is also considered, replacing the Kullback–Leibler distance involved in the empirical likelihood by the Euclidean distance. Both methodologies are tested on simulated data and applied to assess the risk due to the presence of methyl mercury in fish and other seafood.

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