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Forum on Benford’s law and statistical methods for the detection of frauds

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  • Lucio Barabesi

    (University of Siena)

  • Andrea Cerioli

    (University of Parma)

  • Domenico Perrotta

    (European Commission, Joint Research Centre (JRC))

Abstract

No abstract is available for this item.

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  • Lucio Barabesi & Andrea Cerioli & Domenico Perrotta, 2021. "Forum on Benford’s law and statistical methods for the detection of frauds," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 767-778, September.
    • Handle: RePEc:spr:stmapp:v:30:y:2021:i:3:d:10.1007_s10260-021-00588-0
    DOI: 10.1007/s10260-021-00588-0
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    References listed on IDEAS

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    1. Nermina Mumic & Peter Filzmoser, 2021. "A multivariate test for detecting fraud based on Benford’s law, with application to music streaming data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 819-840, September.
    2. Fiscalis Tax Gap Project Group, 2016. "The concept of tax gaps - Report on VAT Gap Estimations," Taxation Studies 0065, Directorate General Taxation and Customs Union, European Commission.
    3. Steven J. Miller, 2015. "Benford's Law: Theory and Applications," Economics Books, Princeton University Press, edition 1, number 10527.
    4. Marco Riani & Aldo Corbellini & Anthony C. Atkinson, 2018. "The Use of Prior Information in Very Robust Regression for Fraud Detection," International Statistical Review, International Statistical Institute, vol. 86(2), pages 205-218, August.
    5. Rousseeuw, Peter & Perrotta, Domenico & Riani, Marco & Hubert, Mia, 2019. "Robust Monitoring of Time Series with Application to Fraud Detection," Econometrics and Statistics, Elsevier, vol. 9(C), pages 108-121.
    6. Domenico Perrotta & Francesca Torti, 2018. "Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(4), pages 641-649, December.
    7. Bart Baesens & Sebastiaan Höppner & Irene Ortner & Tim Verdonck, 2021. "robROSE: A robust approach for dealing with imbalanced data in fraud detection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 841-861, September.
    8. Barabesi, Lucio & Pratelli, Luca, 2020. "On the Generalized Benford law," Statistics & Probability Letters, Elsevier, vol. 160(C).
    9. Francesca Torti & Marco Riani & Gianluca Morelli, 2021. "Semiautomatic robust regression clustering of international trade data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 863-894, September.
    10. Andrea Cerioli & Marco Riani & Anthony C. Atkinson & Aldo Corbellini, 2018. "The power of monitoring: how to make the most of a contaminated multivariate sample," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(4), pages 559-587, December.
    11. Lucio Barabesi & Andrea Cerasa & Andrea Cerioli & Domenico Perrotta, 2018. "Goodness-of-Fit Testing for the Newcomb-Benford Law With Application to the Detection of Customs Fraud," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(2), pages 346-358, April.
    12. Andrea Cerioli & Domenico Perrotta, 2014. "Robust clustering around regression lines with high density regions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(1), pages 5-26, March.
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