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Druica, E, Oancea, B and Valsan, C (2018). Benford's law and the limits of digit analysis. International Journal of Accounting Information Systems 31, pp. 75–82.

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Benedito-Nunes, AM, Gamper, J, Chapman, SC, Friel, M and Gjerloev, JW (2023). Newcomb-Benford Law characterization of solar wind magnetic field and geomagnetic indices. Posted on Authorea; last accessed April 29, 2023. DOI:10.22541/essoar.168121593.35845953/v1. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Caffarini, J, Gjini, K, Sevak, B, Waleffe, R, Kalkach-Aparicio, Poly, M and Struck, AF (2021). Engineering Nonlinear Epileptic Biomarkers Using Deep Learning and Benford’s Law. Preprint posted on ResearchSquare.com; last accessed December 15, 2021. DOI:10.21203/rs.3.rs-1105250/v1. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Carmo, CRS, Caneppele, FdL and Nunes, FC (2021). Analysis of Covid-19 Contamination and Deaths Cases in Brazil According to The Newcomb-Benford Law. Revista Brasileira de Biometria 39(4), pp.522-535. DOI:10.28951/rbb.v39i4.535. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Cerqueti, R and Lupi, C (2022). Severe testing of Benford’s law. Preprint arXiv:2202.05237 [stat.ME]; last accessed February 21, 2022. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Cerqueti, R, Maggi, M and Riccioni, J (2022). Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk. Annals of Operations Research. DOI:10.1007/s10479-022-04742-z. 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
González, F (2019). Detecting Anomalous Data in Household Surveys: Evidence for Argentina. Journal of Social and Economic Statistics 8(2), pp. 1-10. DOI:10.2478/jses-2019-0001. View Complete Reference Online information Works that this work references No Bibliography works reference this work
González, F (2020). Self-reported income data: are people telling the truth?. To appear in Journal of Financial Crime. DOI:10.1108/JFC-08-2019-0113. View Complete Reference Online information Works that this work references Works that reference this work
Hanci, F (2022). Application of Benford’s law in agricultural production statistics. Journal of the National Science Foundation of Sri Lanka 50 (2), pp. 387-393. DOI:10.4038/jnsfsr.v50i2.10429. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Herteliu, C, Jianu, I, Dragan, IM, Apostu, S and Luchian, I (2021). Testing Benford’s Laws (non)conformity within disclosed companies’ financial statements among hospitality industry in Romania. Physica A: Statistical Mechanics and its Applications 582, p. 126221. DOI:10.1016/j.physa.2021.126221. View Complete Reference Online information Works that this work references Works that reference this work
Ileanu, B-V (2021). Time Lag Evidence of Anti-Abortion Decree and Perturbation of Births Distribution. A Benford Law Approach. Preprint arXiv:2106.15520 [physics.soc-ph]; last accessed July 30, 2021. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Jalan, A, Matkovskyy, R and Yarovaya, L (2021). “Shiny” crypto assets: A systemic look at gold-backed cryptocurrencies during the COVID-19 pandemic. International Review of Financial Analysis 78, p. 101958 . DOI:10.1016/j.irfa.2021.101958. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Mahasuar, K (2021). Lies, damned lies, and statistics: The uncertainty over COVID-19 numbers in India. Knowledge and Process Management, pp. 1– 8. DOI:10.1002/kpm.1685. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Mbona, I and Eloff, JHP (2022). Feature selection using Benford’s law to support detection of malicious social media bots. Information Sciences 582, pp. 369-381. DOI:10.1016/j.ins.2021.09.038. View Complete Reference Online information Works that this work references Works that reference this work
Mbona, I and Eloff, JHP (2022). Detecting Zero-day intrusion attacks using semi-supervised machine learning approaches. IEEE Access. DOI:10.1109/ACCESS.2022.3187116. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Mucko, P and Adamczyk, A (2023). Does the bankrupt cheat? Impact of accounting manipulations on the effectiveness of a bankruptcy prediction. PLoS ONE 18(1), e0280384. ISSN/ISBN:1932-6203. DOI:10.1371/journal.pone.0280384. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Orth, CdO, Michaelsen, AT and Lerner, AF (2020). Newcomb Benford law and accounting audit: a systematic literature review. Gestao E Desenvolvimento 17(2), pp. 111-135. DOI:10.25112/rgd.v17i2.2035. SPA View Complete Reference Online information Works that this work references Works that reference this work
Patel, PC, Tsionas, MG and Guedes, MJ (2022). Benford's law, small business financial reporting, and survival. Managerial and Decision Economics. DOI:10.1002/mde.3595. View Complete Reference Online information Works that this work references Works that reference this work
Pinto, SO and Sobreiro, VA (2022). Literature review: Anomaly detection approaches on digital business financial systems. Digital Business 2(2), pp. 100038. ISSN/ISBN:2666-9544. DOI:10.1016/j.digbus.2022.100038. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Pröger, L, Griesberger, P, Hackländer, K, Brunner, N and Kühleitner, M (2021). Benford’s Law for Telemetry Data of Wildlife. Stats 4(4), pp. 943–949. DOI:10.3390/ stats4040055. View Complete Reference Online information Works that this work references Works that reference this work
Sifat, I, van Donselaar, D and Tariq, SA (2023). Suspicious Trading in Nonfungible Tokens (NFTs). Preprint on ResearchGate. DOI:10.13140/RG.2.2.17058.91843. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Silva, AdeA and Gouvêa, MA (2023). Study on the effect of sample size on type I error, in the first, second and first-two digits excessmad tests. International Journal of Accounting Information Systems 48, p. 100599. DOI:10.1016/j.accinf.2022.100599. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Tošić, A and Vičič, J (2021). Use of Benford's law on academic publishing networks. Journal of Informetrics 15(3), 101163. DOI:10.1016/j.joi.2021.101163. View Complete Reference Online information Works that this work references Works that reference this work
Vičič, J and Tošić, A (2021). Application of Benford’s law on cryptocurrencies. Preprints 2021, 2021110472. DOI:10.20944/preprints202111.0472.v1. View Complete Reference Online information Works that this work references Works that reference this work
Vishnu, U (2021). Deepfake Detection using Benford’s Law and Distribution Variance Statistic. International Research Journal of Engineering and Technology(IRJET) 08(10), pp. 712-719. View Complete Reference Online information Works that this work references No Bibliography works reference this work