This work is cited by the following items of the Benford Online Bibliography:
Agyemang, EF, Nortey, ENN, Minkah, R and Asah-Asante, K (2023). Baseline comparative analysis and review of election forensics: Application to Ghana’s 2012 and 2020 presidential elections. Heliyon 9 p. e18276. DOI:10.1016/j.heliyon.2023.e18276. | ||||
Arezzo, MF and Cerqueti, R (2023). A Benford’s Law view of inspections’ reasonability. Physica A: Statistical Mechanics and its Applications 632, Part 1, pp. 129294. DOI:10.1016/j.physa.2023.129294. | ||||
Arshadi, L and Jahangir, AH (2014). Benford's law behavior of Internet traffic. Journal of Network and Computer Applications, Volume 40, April 2014, pp. 194–205. ISSN/ISBN:1084-8045. DOI:10.1016/j.jnca.2013.09.007. | ||||
Avcı, O and Demirci, Z (2016). Benford Kanunu’nun Vergi Denetiminde Kullanımı Ve Bir Örnek Uygulama [Use of Benford's Law in Tax Auditing and A Sample Application] . İnsan ve Toplum Bilimleri Araştırması Dergisi 5(7), pp. 2232-2246. DOI:10.15869/itobiad.260262. TUR | ||||
Badal-Valero, E, Alvarez-Jareño, JA and Pavía, JM (2018). Combining Benford's Law and machine learning to detect money laundering. An actual Spanish court case. Forensic Science International 282, pp. 24-34. DOI:10.1016/j.forsciint.2017.11.008. | ||||
Barabesi, L, Cerasa, A, Cerioli, A and Perrotta, D (2018). Goodness-of-fit testing for the Newcomb-Benford law with application to the detection of customs fraud. Journal of Business & Economic Statistics 36(2), pp. 346-358. DOI:10.1080/07350015.2016.1172014. | ||||
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. | ||||
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. | ||||
Bond, KD, Conrad, CR, Moses, D and Simmons, JW (2021). Detecting anomalies in data on government violence. Political Science Research and Methods, pp. 1-8. DOI:10.1017/psrm.2021.40. | ||||
Branets, S (2019). Detecting money laundering with Benford’s law and machine learning . Masters Thesis, University of Tartu. | ||||
Brock, T (2014). Benford’s law and elections – part 2. Posted on Datatodisplay.com blog; last accessed April 25, 2019. | ||||
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. DOI:10.1073/pnas.1806617115. | ||||
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. | ||||
Cerqueti, R and Maggi, M (2021). Data validity and statistical conformity with Benford’s Law. Chaos, Solitons & Fractals 144, p. 110740 . DOI:10.1016/j.chaos.2021.110740. | ||||
Charoenwong, B and Reddy, P (2022). Using forensic analytics and machine learning to detect bribe payments in regime-switching environments: Evidence from the India demonetization. PLoS ONE 17(6): e0268965. DOI:10.1371/journal.pone.0268965. | ||||
Chen, T and Tsourakakis, CE (2022). AntiBenford Subgraphs: Unsupervised Anomaly Detection in Financial Networks. Preprint arXiv:2205.13426 [cs.; last accessed June 9, 2022. | ||||
Cuenca, AV (2023). La Ley de Benford, Del Primer Dígito Significativo. Trabajo Fin de Grado en Matemáticas, Universidad de Valladolid . SPA | ||||
Filho, DF, Silva, L and Carvalhoa, E (2022). The forensics of fraud: Evidence from the 2018 Brazilian presidential election. Forensic Science International: Synergy, p. 100286. ISSN/ISBN:2589-871X. DOI:10.1016/j.fsisyn.2022.100286. | ||||
Fonseca, PMT da (2016). Digit analysis using Benford's Law: A Bayesian approach. Masters Thesis, ISEG - Instituto Superior de Economia e Gestão, Lisbon School of Economics & Management, Portugal. | ||||
Guliyev, H (2021). COVID-19 Data Published by Turkey is Fake or Not?. Preprint on ResearchSquare.com. Published in Systematic Reviews in Pharmacy 13(1), pp. 24-29 (2022).. | ||||
Huang, Y, Niu, Z and Yang, C (2020). Testing firm-level data quality in China against Benford’s Law. Economics Letters 192, 109182. DOI:10.1016/j.econlet.2020.109182. | ||||
Hulme, PE, Ahmed, DA, Haubrock, PJ, Kaiser, BA, Kourantidou, M, Leroy, B and McDermott, SM (2023). Widespread imprecision in estimates of the economic costs of invasive alien species worldwide. Science of the Total Environment, pp. 167997. DOI:10.1016/j.scitotenv.2023.167997. | ||||
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. | ||||
Jiménez, R and Hidalgo, M (2014). Forensic Analysis of Venezuelan Elections during the Cha ́vez Presidency. PLOS ONE 9(6), pp. 1-18. DOI:10.1371/journal.pone.0100884. | ||||
Joenssen, DW (2014). Testing for Benford's Law: A Monte Carlo Comparison of Methods. Preprint available at SSRN: https://ssrn.com/abstract=2545243; last accessed Mar 24, 2019 . DOI:10.2139/ssrn.2545243. | ||||
Joksimović, D, Knežević, G, Pavlović, V, Ljubić, M and Surovy, V (2017). Some Aspects of the Application of Benford’s Law in the Analysis of the Data Set Anomalies. In: Knowledge Discovery in Cyberspace: Statistical Analysis and Predictive Modeling. New York: Nova Science Publishers, pp. 85–120. ISSN/ISBN:978-1-53610-566-7. | ||||
Jošić , H and Žmuk, B (2020). The Application of the Law of Anomalous Numbers on Global Food Prices in Examining Psychological Pricing Strategies. Journal of International Food & Agribusiness Marketing, pp. 1-16. DOI:10.1080/08974438.2020.1796880 . | ||||
Kalinin, K and Mebane, WR Jr (2017). When the Russians fake their election results, they may be giving us the statistical finger. The Washington Post January 11, 2017. | ||||
Klimek, P, Jiménez, R, Hidalgo, M, Hinteregger, A and Thurner, S (2018). Forensic analysis of Turkish elections in 2017–2018. PLoS ONE 13(10), pp. e0204975. DOI:10.1371/journal.pone.0204975. | ||||
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. | ||||
Lacasa, L and Fernández-Gracia, J (2019). Election Forensics: Quantitative methods for electoral fraud detection. Forensic Science International 294, pp. e19-e22. DOI:10.1016/j.forsciint.2018.11.010. | ||||
Li, F, Han, S, Zhang, H, Ding, J, Zhang, J and Wu, J (2019). Application of Benford’s law in Data Analysis. Journal of Physics: Conference Series 1168, pp. 032133. DOI:10.1088/1742-6596/1168/3/032133. | ||||
Mack, V (2016). The Fingerprints of Fraud: An In-depth Study of Election Forensics with Digit Tests. PhD Thesis, Universitat Konstanz. | ||||
Mainusch, NM (2020). On Benford's law - Computing a Bayes factor with the Savage-Dickey method to quantify conformance of numerical data to Benford's law. Bachelor's Thesis, University of Osnabrueck, Institute of Cognitive Science, Germany. | ||||
Mebane, WR Jr (2012). Second-digit Tests for Voters’ Election Strategies and Election Fraud. Prepared for presentation at the 2012 Annual Meeting of the Midwest Political Science Association, Chicago, April 12–15; last accessed Apr 11, 2019. | ||||
Mebane, WR Jr (2013). Election Forensics: The Meanings of Precinct Vote Counts’ Second Digits. Prepared for presentation at the 2013 Summer Meeting of the Political Methodology Society, University of Virginia, July 18–20. | ||||
Mebane, WR Jr (2013). Using Vote Counts’ Digits to Diagnose Strategies and Frauds: Russia. Prepared for presentation at the 2013 Annual Meeting of the American Political Science Asso- ciation, Chicago, August 29–September 1, 2013. | ||||
Mebane, WR Jr and Kent, T (2013). Second digit implications of voters’ strategies and mobilizations in the United States during the 2000s. Proceedings of the 2013 Annual Meeting of the Midwest Political Science Association, Chicago, IL, April 11–14. | ||||
Mebane, WR Jr and Klaver, J (2015). Election Forensics: Strategies versus Election Frauds in Germany. Prepared for presentation at the 2015 Annual Conference of the European Political Science Association, Vienna, Austria, June 25–27. | ||||
Medzihorsky, J (2015). Election Fraud: A Latent Class Framework for Digit-Based Tests. Political Analysis 23(4), pp. 506-517. DOI:10.1093/pan/mpv021. | ||||
Miller, SJ (ed.) (2015). Benford's Law: Theory and Applications. Princeton University Press: Princeton and Oxford. ISSN/ISBN:978-0-691-14761-1. | ||||
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. | ||||
Parreño, SJE (2023). Assessing the quality of dengue data in the Philippines using Newcomb-Benford law. Sapienza: International Journal of Interdisciplinary Studies 4(3). DOI:10.51798/sijis.v4i3.662. | ||||
Pierzgalski, M (2018). Odkrywanie fałszerstw wyborczych a „prawo” Benforda [Discovering Election Fraud and Benford’s “Law”]. Preprint, last accessed Apr 25, 2019. DOI:10.14746/ssp.2018.1.7. POL | ||||
Sadaf, R (2017). Advanced Statistical Techniques For Testing Benford'S Law. Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(2), pp. 229-238. | ||||
Silva, LEdO and Figueiredo, D (2024). A novel approach to evaluate data integrity: evidence from COVID-19 in China. Brazilian Journal of Biometrics 42(1), pp. 78-87. DOI:10.28951/bjb.v42i1.659. | ||||
Tunmibi, S and Olatokun, W (2020). Application of digits based test to analyse presidential election data in Nigeria. Commonwealth & Comparative Politics 59(1), pp. 1-24. DOI:10.1080/14662043.2020.1834743. | ||||
Vovor-Dassu, KC (2021). Tests d'adéquation à la loi de Newcomb-Benford comme outils de détection de fraudes. PhD Thesis L’Universite de Montpellier. DOI:10.13140/RG.2.2.12559.25764. FRE | ||||
Wang, H, Liu, T, Zhang, Y, Wu, Y, Sun, Y, Dong, J and Huang, W (2023). Last Digit Tendency: Lucky Numbers and Psychological Rounding in Mobile Transactions. Fundamental Research. DOI:10.1016/j.fmre.2023.11.011. | ||||
Wang, L and Ma, B-Q (2023). A concise proof of Benford’s law. Fundamental Research . DOI:10.1016/j.fmre.2023.01.002. |