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

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  • Amélie Crepet

    (Crest)

  • Hugo Harari-Kermadec

    (Crest)

  • Jessica Tressou

    (Crest)

Abstract

This paper introduces an original methodology based on empirical likelihood which aims at combiningdifferent contamination and consumptions surveys in order to provide risk managers witha risk measure taking account of all the available information. This risk index is defined as theprobability that exposure to a contaminant exceeds a safe dose. It is expressed as a non linearfunctional of the different consumption and contamination distributions, more precisely as a generalizedU-statistic. This non linearity and the huge size of the data sets make direct computation ofthe problem unfeasible. Using linearization techniques and incomplete versions of the U-statistic,a tractable "approximated" empirical likelihood program is solved yielding asymptotic confidenceintervals for the risk index. An alternative "Euclidean likelihood program" is also considered, replacingthe 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 presenceof methyl mercury in fish and other seafoods.

Suggested Citation

  • Amélie Crepet & Hugo Harari-Kermadec & Jessica Tressou, 2007. "Using Empirical Likelihood to Combine Data : Application to Food Risk Assessment," Working Papers 2007-20, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2007-20
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    References listed on IDEAS

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    1. Whitney K. Newey & Richard J. Smith, 2004. "Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators," Econometrica, Econometric Society, vol. 72(1), pages 219-255, January.
    2. Patrice Bertail & Jessica Tressou, 2006. "Incomplete Generalized U-Statistics for Food Risk Assessment," Biometrics, The International Biometric Society, vol. 62(1), pages 66-74, March.
    3. Judith K. Hellerstein & Guido W. Imbens, 1999. "Imposing Moment Restrictions From Auxiliary Data By Weighting," The Review of Economics and Statistics, MIT Press, vol. 81(1), pages 1-14, February.
    4. Antoine, Bertille & Bonnal, Helene & Renault, Eric, 2007. "On the efficient use of the informational content of estimating equations: Implied probabilities and Euclidean empirical likelihood," Journal of Econometrics, Elsevier, vol. 138(2), pages 461-487, June.
    5. Paul S. Price & Cynthia L. Curry & Philip E. Goodrum & Michael N. Gray & Jane I. McCrodden & Natalie W. Harrington & Heather Carlson‐Lynch & Russell E. Keenan, 1996. "Monte Carlo Modeling of Time‐Dependent Exposures Using a Microexposure Event Approach," Risk Analysis, John Wiley & Sons, vol. 16(3), pages 339-348, June.
    6. Andrew Chesher, 1997. "Diet Revealed?: Semiparametric Estimation of Nutrient Intake–Age Relationships," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 389-428, September.
    7. Tressou, Jessica, 2006. "Nonparametric Modeling of the Left Censorship of Analytical Data in Food Risk Assessment," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1377-1386, December.
    Full references (including those not matched with items on IDEAS)

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