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. 594-616 . DOI:10.1017/S0269964823000207.
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Barabesi, L, Cerasa, A, Cerioli, A and Perotta, D (2021). A combined test of the Benford Hypothesis With Anti-fraud Applications. Proceedings of 13th Scientific Meeting of the Classification and Data Analysis Group, Florence, September 9-11. STAMPA, pp. 256-259. DOI:10.36253/978-88-5518-340-6.
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Barabesi, L, Cerioli, A and Di Marzio, M (2023). Statistical models and the Benford hypothesis: a unified framework. TEST. DOI:10.1007/s11749-023-00881-y.
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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/s10260-021-00588-0.
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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.
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Cerioli, A, Barabesi, L, Cerasa, A and Perrotta, D (2022). Who is afraid of the probability-savvy fraudster?. Conference presentation at MBC2 2022 Models and Learning for Clustering and Classification 6th International Workshop, Catania.
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Kössler, W, Lenz, H-J 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.rs-3336839/v1.
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