Cerioli, A, Barabesi, L, Cerasa, A and Perrotta, D (2022). Who is afraid of the probabilitysavvy fraudster?. Conference presentation at MBC2 2022 Models and Learning for Clustering and Classification 6th International Workshop, Catania.
This work cites the following items of the Benford Online Bibliography:
Barabesi, L, Cerasa, A, Cerioli, A and Perrotta, D (2018). Goodnessoffit testing for the NewcombBenford law with application to the detection of customs fraud. Journal of Business & Economic Statistics 36(2), pp. 346358. DOI:10.1080/07350015.2016.1172014.





Barabesi, L, Cerasa, A, Cerioli, A and Perrotta, D (2021). On characterizations and tests of Benfordâ€™s law. Journal of the American Statistical Association. DOI:10.1080/01621459.2021.1891927.





Berger, A and Hill, TP (2015). An Introduction to Benford's Law. Princeton University Press: Princeton, NJ. ISSN/ISBN:9780691163062.





Cerioli, A, Barabesi, L, Cerasa, A, Menegatti, M and Perrotta, D (2019). NewcombBenford law and the detection of frauds in international trade. Proceedings of the National Academy of Sciences 116(1), pp. 106115. DOI:10.1073/pnas.1806617115.





Nigrini, MJ (2012). Benford's Law: Applications for Forensic Accounting, Auditing, and Fraud Detection . John Wiley & Sons: Hoboken, New Jersey. ISSN/ISBN:9781118152850. DOI:10.1002/9781119203094.





Pericchi, LR and Torres, DA (2011). Quick anomaly detection by the NewcombBenford law, with applications to electoral processes data from the USA, Puerto Rico and Venezuela. Statistical Science 26(4), pp. 50216. DOI:10.1214/09STS296.




