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Cerioli, A, Barabesi, L, Cerasa, A, Menegatti, M and Perrotta, D (2019). Newcomb-Benford law and the detection of frauds in international trade. Proceedings of the National Academy of Sciences 116(1), pp. 106-115.

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Azevedo, CdS, Gonçalves, RF, Gava, VL and Spinola, MdM (2021). A Benford’s Law based methodology for fraud detection in social welfare programs: Bolsa Familia analysis. Physica A 567, p. 125626. DOI:10.1016/j.physa.2020.125626. 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, Cerasa, A, Cerioli, A and Perrotta, D (2021). On characterizations and tests of Benford’s law. Journal of the American Statistical Association. DOI:10.1080/01621459.2021.1891927. 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 No Bibliography works reference this work
Barabesi, L and Pratelli, L (2020). On the Generalized Benford law. Statistics & Probability Letters 160, 108702 . DOI:10.1016/j.spl.2020.108702. 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 No Bibliography works 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
Chen, T and Tsourakakis, CE (2022). AntiBenford Subgraphs: Unsupervised Anomaly Detection in Financial Networks. Preprint arXiv:2205.13426 [cs.; last accessed June 9, 2022. View Complete Reference Online information Works that this work references No Bibliography works reference this work
D'Alessandro, A (2020). Benford's law and metabolomics: A tale of numbers and blood. Transfusion and Apheresis Science 59(6), pp. 103019. DOI:10.1016/j.transci.2020.103019. View Complete Reference Online information Works that this work references Works that reference this work
Ducharme, RG, Kaci, S and Vovor-Dassu ,C (2020). Smooths Tests of Goodness-of-fit for the Newcomb-Benford distribution. Preprint: arXiv:2003.00520v1 [math.ST]. FRE View Complete Reference Online information Works that this work references No Bibliography works reference this work
Ensminger, J and Leder-Luis, J (2022). Measuring Strategic Data Manipulation: Evidence from a World Bank Project. Preprint, submitted for publication. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Ensminger, J and Leder-Luis, J (2022). Detecting Fraud in Development Aid. Preprint. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Filho, DF, Silva, L and Medeiros, H (2022). “Won’t get fooled again”: statistical fault detection in COVID-19 Latin American data. Globalization and Health 18, pp.105. DOI:10.1186/s12992-022-00899-1. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Kalameyets, M, Levshun, D, Soloviev, S, Chechulin, A and Kotenko, I (2020). Social networks bot detection using Benford’s law. SIN 2020: 13th International Conference on Security of Information and Networks, Article No.: 19. pp. 1–8. DOI:10.1145/3433174.3433589. View Complete Reference Online information Works that this work references Works that reference this work
Kennedy, AP and Yam, SCP (2020). On the authenticity of COVID-19 case figures. PLoS ONE 15(12): e0243123. DOI:10.1371/journal.pone.0243123. View Complete Reference Online information Works that this work references Works that reference this work
Lacasa, L (2019). Newcomb–Benford law helps customs officers to detect fraud in international trade. Proceedings of the National Academy of Sciences 116(1), pp. 11-13. DOI:10.1073/pnas.1819470116. View Complete Reference Online information Works that this work references Works that reference this work
Martínez JW, Martínez JC, Rincón DA, Salazar, DA, Castrillón JD, Gómez MDP, Suárez OF, Vélez JP, Valencia ÁM, Gómez S, Rincón ÁM, Idrovo ÁJ, Moreno-Montoya J, Prieto-Alvarado FE, Hurtado-Ortiz A and (2020). Benchmarking of public health surveillance of COVID-19 in Colombia: First semester. Biomedica : Revista del Instituto Nacional de Salud 40(Supl. 2), pp. 198-204. SPA View Complete Reference Online information Works that this work references No Bibliography works reference this work
Morag, S and Salmon-Divon, M (2019). Characterizing Human Cell Types and Tissue Origin Using the Benford Law. Cells 8(9), p. 1004. DOI:10.3390/cells8091004. View Complete Reference Online information Works that this work references Works that reference this work
Moreau, VH (2021). Inconsistencies in Countries COVID-19 Data Revealed by Benford’s Law’. Model Assisted Statistics and Applications 16(1), pp. 73-79. DOI:10.3233/MAS-210517. View Complete Reference Online information Works that this work references Works that reference this work
Morillas-Jurado, FG, Caballer-Tarazona, M and Caballer-Tarazona, V (2022). Applying Benford’s Law to Monitor Death Registration Data: A Management Tool for the COVID-19 Pandemic. Mathematics 10(1), 46. DOI:10.3390/math10010046. View Complete Reference Online information Works that this work references Works that reference this work
Mumic, N and Filzmoser, P (2021). A multivariate test for detecting fraud based on Benford’s law, with application to music streaming data. Statistical Methods & Applications. DOI:10.1007/s10260-021-00582-6. View Complete Reference Online information Works that this work references Works that reference this work
Perrotta, D, Cerasa, A, Barabesi, L and Menegatti, M (2019). Contamination And Manipulation Of Trade Data: The Two Faces Of Customs Fraud . Book of Short Papers, Proceedings of the 12th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS), pp. 394-397. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Renaldo, N, Hutahuruk, MB and Putri, IY (2022). Forensic Accounting: The Use of Benford's Law to Evaluate Indications of Fraud . Revista Eletrônica do Departamento de Ciências Contábeis & Departamento de Atuária e Métodos Quantitativos (REDECA) 9(e57343), pp. 1-15. DOI:10.23925/2446-9513.2022v9id57343. View Complete Reference No online information available Works that this work references No Bibliography works reference this work
Wang, L and Bo-Qiang, M (2023). A concise proof of Benford’s law. Fundamental Research . DOI:10.1016/j.fmre.2023.01.002. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Zhang, J (2020). Testing Case Number of Coronavirus Disease 2019 in China with Newcomb-Benford Law. Preprint arXiv:2002.05695 [physics.soc-ph]; last accessed February 18, 2020. View Complete Reference Online information Works that this work references Works that reference this work