Barabesi, L, Cerasa, A, Cerioli, A and Perrotta, D (2021). On characterizations and tests of Benford’s law. Journal of the American Statistical Association.
This work is cited by the following items of the Benford Online Bibliography:
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Balado, F and Silvestre, GCM (2024). General Distributions of Number Representation Elements. Probability in the Engineering and Informational Sciences 38(3), pp. 594616 . DOI:10.1017/S0269964823000207.





Barabesi, L, Cerasa, A, Cerioli, A and Perotta, D (2021). A combined test of the Benford Hypothesis With Antifraud Applications. Proceedings of 13th Scientific Meeting of the Classification and Data Analysis Group, Florence, September 911. STAMPA, pp. 256259. DOI:10.36253/9788855183406.





Barabesi, L, Cerioli, A and Di Marzio, M (2023). Statistical models and the Benford hypothesis: a unified framework. TEST. DOI:10.1007/s1174902300881y.





Barabesi, L, Cerioli, A and Perrotta, D (2021). Forum on Benford’s law and statistical methods for the detection of frauds. Statistical Methods & Applications 30, pp. 767–778. DOI:10.1007/s10260021005880.





Cerasa, A (2022). Testing for Benford’s Law in very small samples: Simulation study and a new test proposal. PLoS ONE 17(7), pp. e0271969. DOI:10.1371/journal.pone.0271969.





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.





Kössler, W, Lenz, HJ and Wang, XD (2023). Some new invariant sum tests and MAD tests for the assessment of Benford's Law. Preprint on ResearchSquare. DOI:10.21203/rs.3.rs3336839/v1.




