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Barabesi, L, Cerasa, A, Cerioli, A and Perrotta, D (2021). On characterizations and tests of Benford’s law. Journal of the American Statistical Association 117(540), pp. 1887-1903.

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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. View Complete Reference Online information Works that this work references Works that reference this work
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. View Complete Reference Online information Works that this work references No Bibliography works reference this work
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. View Complete Reference Online information Works that this work references Works that reference this work
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. View Complete Reference Online information Works that this work references Works that reference this work
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. View Complete Reference Online information Works that this work references Works that reference this work
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. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Cumbrera-Conde, I, Silva-Ramírez, E-L and Cumbrera, FL (2024). Signal processing analysis for detection of anomalies in numerical series. Expert Systems with Applications 255 Part D, pp. 124708. DOI:10.1016/j.eswa.2024.124708. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Di Marzio, M, Fensore, S and Passamonti, C (2024). Validating Benfordness on contaminated data. Socio-Economic Planning Sciences 95, pp. 102008. DOI:10.1016/j.seps.2024.102008. View Complete Reference Online information Works that this work references Works that reference this work
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. View Complete Reference Online information Works that this work references Works that reference this work
Sarkandiz, MR (2024). Identifying the Hidden Nexus between Benford Law Establishment in Stock Market and Market Efficiency: An Empirical Investigation. Preprint arXiv:2501.02674 [econ.GN] last accessed January 24, 2025. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Wang, Z, Xu, G and Ren, M (2025). Can attention detect AI-generated text? A novel Benford's law-based approach. Information Processing & Management 62(4), pp. 104139. DOI:10.1016/j.ipm.2025.104139. View Complete Reference Online information Works that this work references No Bibliography works reference this work