Cross Reference Up

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:

Note that this list may be incomplete, and is currently being updated. Please check again at a later date.


Balado, F and Sylvestre, G (2023). General Distributions of Number Representation Elements. Preprint arXiv:2301.10547 [math.PR]; last accessed April 29, 2023. 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 No Bibliography works 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
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 No Bibliography works reference this work