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Searching for contaminants

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  • Nicholas T. Longford

Abstract

Decision theory is applied to the problem of identifying a small fraction of observations that contaminate a random sample from a specified distribution. The uncertainty about the parameters that characterise the contamination is addressed by sensitivity analysis. The analyst's (or the client's) perspective and priorities are incorporated in the analysis by ranges of plausible loss functions. An application to fraud detection is presented.

Suggested Citation

  • Nicholas T. Longford, 2013. "Searching for contaminants," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(9), pages 2041-2055, September.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:2041-2055
    DOI: 10.1080/02664763.2013.804041
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    References listed on IDEAS

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    1. N. T. Longford & Pierpaolo D'Urso, 2011. "Mixture models with an improper component," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2511-2521, January.
    2. Nicholas Longford, 2009. "Analysis of all-zero binomial outcomes with borderline and equilibrium priors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1259-1265.
    3. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    4. S. Lalitha & Nirpeksh Kumar, 2012. "Multiple outlier test for upper outliers in an exponential sample," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(6), pages 1323-1330, November.
    5. Longford, Nicholas T., 2010. "Bayesian Decision Making About Small Binomial Rates With Uncertainty About the Prior," The American Statistician, American Statistical Association, vol. 64(2), pages 164-169.
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