This work is cited by the following items of the Benford Online Bibliography:
Akkaş, ME (2015). Altın Getirileri Dağılımının Newcomb-Benford Kanunu İle Testi [Testing Distribution of Gold Returns by Newcomb-Benford Law]. Uluslararası Sosyal Araştırmalar Dergisi 8(40), pp. 577-584. DOI:10.17719/jisr.20154013940. TUR | ||||
Anab, F, Khaliq, A and Younas, I (2021). A Statistical Analysis of Covid-19 Data of Pakistan by Applying Benford’s Law. Journal of Applied Pharmacy 13, pp. 55-60. | ||||
Anderson, KM, Dayaratna, K, Gonshorowski, D and Miller, SJ (2022). A New Benford Test for Clustered Data with Applications to American Elections. Stats 5(3), pp. 841–855. DOI:10.3390/stats5030049 . | ||||
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. | ||||
Bak-Coleman, J, Wack, M, Schafer, J, Spiro, E and West, J (2020). Vote Data Patterns used to Delegitimize the Election Results. Posted on EIPartnership.net November 6, 2020; last accessed December 1, 2020. NOTE: Some information in this posting has been disputed. Please see https://arxiv.org/abs/2011.13015 for details. | ||||
Balashov, VS, Yan, Y and Zhu, X (2020). Who Manipulates Data During Pandemics? Evidence from Newcomb-Benford Law. Preprint arXiv:2007.14841 [econ.GN]; last accessed March 10, 2021. | ||||
Ball, J (2012). Russian election: does the data suggest Putin won through fraud?. The Guardian, March 5, 2012. | ||||
Beber, B and Scacco, A (2012). What the Numbers Say: A Digit-Based Test for Election Fraud. Political Analysis 20 (2), pp. 211-234. DOI:10.1093/pan/mps003. | ||||
Berger, A and Eshun, G (2014). Benford solutions of linear difference equations. Theory and Applications of Difference Equations and Discrete Dynamical Systems, Springer Proceedings in Mathematics & Statistics Volume 102, pp. 23-60. ISSN/ISBN:978-3-662-44139-8. DOI:10.1007/978-3-662-44140-4_2. | ||||
Berger, A and Hill, TP (2015). An Introduction to Benford's Law. Princeton University Press: Princeton, NJ. ISSN/ISBN:9780691163062. | ||||
Blondeau Da Silva, S (2019). Benford or Not Benford: A Systematic But Not Always Well-Founded Use of an Elegant Law in Experimental Fields. Communications in Mathematics and Statistics, pp. 1-35. ISSN/ISBN:2194-6701. DOI:10.1007/s40304-018-00172-1. | ||||
Blondeau Da Silva, S (2020). Limits of Benford’s Law in Experimental Field. International Journal of Applied Mathematics 33(4), pp. 685-695. DOI:10.12732/ijam.v33i4.12. | ||||
Blondeau Da Silva, S (2022). An Alternative to the Oversimplifying Benford’s Law in Experimental Fields. Sankhya B. DOI:10.1007/s13571-022-00287-0. | ||||
Brock, T (2014). Benford’s law and elections – part 2. Posted on Datatodisplay.com blog; last accessed April 25, 2019. | ||||
Brown, MS (2012). Does the Application of Benford's Law Reliably Identify Fraud on Election Day? . Masters thesis, Georgetown University. | ||||
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. | ||||
Chamberlain, A and Yanus, AB (2021). Evaluating Federated Voluntary Associations’ Membership Data: An Application of Benford's Law. Social Science Quarterly pp. 1– 12. DOI:10.1111/ssqu.13015. | ||||
Coeurjolly, J-F (2020). Digit analysis for Covid-19 reported data . Preprint arXiv:2005.05009 [stat.AP]; last accessed May 17, 2020. | ||||
Costa, JI (2012). Desenvolvimento de metodologias contabilométricas aplicadas a auditoria contábil digital: uma proposta de análise da lei de Newcomb-Benford para os Tribunais de Contas. Thesis, Universidade Federal de Pernambuco, Recife, Brasil. POR | ||||
da Silva, ASCD (2013). The application of Benford’s Law in detecting accounting fraud in the Financial Sector. Masters Thesis, Lisboa School of Economics & Management. | ||||
Dacey, J (2020). Benford’s law and the 2020 US presidential election: nothing out of the ordinary. PhysicsWorld Everyday Science Blog posted 19 Nov 2020; last accessed 15 Dec 2021. | ||||
Dutta, A, Choudhury, MR and De, AK (2022). A Unified Approach to Fraudulent Detection. International Journal of Applied Engineering Research 17(2), pp. 110-124. ISSN/ISBN:0973-4562. | ||||
Ensminger, J and Leder-Luis, J (2022). Measuring Strategic Data Manipulation: Evidence from a World Bank Project. Preprint, submitted for publication. | ||||
Ensminger, J and Leder-Luis, J (2022). Detecting Fraud in Development Aid. Preprint. | ||||
Fernández-Gracia, J and Lacasa, L (2018). Bipartisanship Breakdown, Functional Networks, and Forensic Analysis in Spanish 2015 and 2016 National Elections. Complexity 2018, Article ID 9684749. DOI:10.1155/2018/9684749. | ||||
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. | ||||
Friedman, E, Kolakaluri, R and Rege, M (2020). Benford’s Law Applied to Precinct Level Election Data. Issues in Information Systems 21(2), pp. 238-247. | ||||
Glen, S (2020). Fraudulent Covid-19 Data and Benford's Law. Blog posted on December 31; last accessed February 15, 2021. | ||||
Golbeck, J (2020). Benford’s Law Does Not Prove Fraud in the 2020 US Presidential Election. Blog posted Nov 10, 2020; last accessed December 15, 2021. NOTE: Some information in this posting has been disputed. Please see https://arxiv.org/abs/2011.13015 for details. | ||||
Goodman, WM (2013). Reality Checks for a Distributional Assumption: The Case of “Benford’s Law”. JSM Proceedings. Alexandria, VA: American Statistical Association (2013), pp. 2789-2803. (Also published on the Statistical Literacy website, at URL: http://www.statlit.org/pdf/2013-Goodman-ASA.pdf) . | ||||
Guliyev, H (2021). COVID-19 Data Published by Turkey is Fake or Not?. Preprint on ResearchSquare.com. | ||||
Hartmann, S and Brinkert, D (2018). Aufdeckung von Versicherungsbetrug bei Kfz-Schäden mit Hilfe des Benford-Tests [Detecting insurance fraud for vehicle damage using the Benford test]. Zeitschrift für die gesamte Versicherungswissenschaft 107(4), pp. 41-59. DOI:10.1007/s12297-017-0396-8. GER | ||||
Holz, CA (2013). The Quality of China's GDP Statistics. Munich Personal RePEc Archive Paper No. 51864; available online at http://mpra.ub.uni-muenchen.de/51864/; last accessed June 23, 2014. | ||||
Holz, CA (2014). The quality of China’s GDP statistics. China Economic Review, vol. 30, September 2014, pp. 309–338. DOI:10.1016/j.chieco.2014.06.009. | ||||
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. | ||||
Hussein, A (2019). An evaluation of last digit-based test as a tool for electoral fraud detection. Bachelor Thesis in Statistics, University of Gothenburg. | ||||
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. | ||||
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 . | ||||
Jošić, H and Žmuk, B (2018). The Application of Benford’s Law in psychological pricing detection. Zbornik radova Ekonomskog fakulteta Sveučilišta u Mostaru, No. 24, pp. 37-57. | ||||
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. | ||||
Kilani, A (2021). An interpretation of reported COVID-19 cases in post-Soviet states. Journal of Public Health, fdab091. DOI:10.1093/pubmed/fdab091. | ||||
Kilani, A (2021). Authoritarian regimes' propensity to manipulate Covid-19 data: a statistical analysis using Benford's Law. Commonwealth & Comparative Politics . DOI:10.1080/14662043.2021.1916207. | ||||
Klimek, P, Yegorov, Y, Hanel, R and Thurner, S (2012). Statistical detection of systematic election irregularities. Proceedings of the National Academy of Science October 109(41), pp. 16469-16473. DOI:10.1073/pnas.1210722109. | ||||
Kossovsky, AE (2014). Benford's Law: Theory, the General Law of Relative Quantities, and Forensic Fraud Detection Applications. World Scientific Publishing Company: Singapore. ISSN/ISBN:978-981-4583-68-8. | ||||
Kossovsky, AE (2014). Arithmetical Tugs of War and Benford's Law. Preprint arXiv:1410.2174 [math.ST]; last accessed October 19, 2020. | ||||
Kossovsky, AE (2015). Random Consolidations and Fragmentations Cycles Lead to Benford' Law. Preprint arXiv:1505.05235 [math.ST]; last accessed October 19, 2020. | ||||
Kossovsky, AE (2016). Exponential Growth Series and Benford's Law. Preprint arXiv:1606.04425 [math.ST]; last accessed October 19, 2020. | ||||
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. | ||||
Leder-Luis, J (2020). The Economics of Fraud and Corruption. PhD Thesis, Massachusetts Institute of Technology. | ||||
Leemann, L and Bochsler, D (2014). A systematic approach to study electoral fraud. Electoral Studies, Vol. 35, Num. 0, pp. 33-47. ISSN/ISBN:0261-3794. DOI:10.1016/j.electstud.2014.03.005. | ||||
MacDonald, DK (2021). Using Benford’s Law to Detect Fraud. Posted on dustinkmacdonald.com blog May 2; last accessed May 18, 2021. | ||||
Mack, V (2016). The Fingerprints of Fraud: An In-depth Study of Election Forensics with Digit Tests. PhD Thesis, Universitat Konstanz. | ||||
Mack, V and Stoetzer, LF (2019). Election fraud, digit tests and how humans fabricate vote counts - An experimental approach. Electoral Studies 58, pp. 31-47 . DOI:10.1016/j.electstud.2018.12.002. | ||||
Mansouri, E, Mostajabi, A, Schulz, W, Diendorfer, G, Rubinstein, M and Rachidi , F (2022). On the Use of Benford’s Law to Assess the Quality of the Data Provided by Lightning Locating Systems. Atmosphere 13(4), pp. 552. ISSN/ISBN:2073-4433. DOI:10.3390/atmos13040552. | ||||
McGinty, JC (2020). Can an Accounting Tool Detect Election Fraud?. Wall Street Journal, Dec 4, 2020. | ||||
Mebane, WR Jr (2011). Comment on “Benford's Law and the Detection of Election Fraud”. Political Analysis 19(3), pp. 269-272. DOI:10.1093/pan/mpr024. | ||||
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 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. | ||||
Mocnik, F-J (2021). Benford's law and geographical information - the example of OpenStreetMap. International Journal of Geographical Information Science. DOI:10.1080/13658816.2020.1829627. | ||||
Morzy, M, Kajdanowicz, T and Szymański, BK (2016). Benford’s Distribution in Complex Networks. Scientific Reports 6:34917. DOI:1038/srep34917. | ||||
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. | ||||
Parker, M (2020). Why do Biden's votes not follow Benford's Law?. Posted on youTube.com November 10, 2020; last accessed December 1, 2020. NOTE: Some information in this posting has been disputed. Please see https://arxiv.org/abs/2011.13015 for details. | ||||
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 | ||||
Rauch, B, Göttsche, M and Langenegger, S (2014). Detecting Problems in Military Expenditure Data Using Digital Analysis. Defence and Peace Economics 25(2), pp. 97-111. DOI:10.1080/10242694.2013.763438. | ||||
Reuters Staff (2020). Fact check: Deviation from Benford’s Law does not prove election fraud. Posted on Reuters.com, November 10; last accessed December 1, 2020. NOTE: Some information in this posting has been disputed. Please see https://arxiv.org/abs/2011.13015 for details. | ||||
Rubin, AE (2021). Benford’s law: Applications to ordinary-chondrite mass distributions. Meteoritics & Planetary Science, pp. 1-14. DOI:10.1111/maps.13626. | ||||
Tunmibi, S and Olatokun, W (2020). Application of digits based test to analyse presidential election data in Nigeria. Commonwealth & Comparative Politics. DOI:10.1080/14662043.2020.1834743. | ||||
Vellwock, AE and Wei, A (2020). On the Benfordness of academic citations. Preprint posted on research gate.net; last accessed March 16, 2021. DOI:10.13140/RG.2.2.22108.82562. | ||||
Wei, A and Vellwock, AE (2020). Is COVID-19 data reliable? A statistical analysis with Benford's Law. Preprint, posted September. DOI:10.13140/RG.2.2.31321.75365/1. | ||||
Xu, X and Zeng, Z (2020). Did Alibaba Fake the Tmall “Double Eleven” Data? Evidence from Benford’s Law. In: Xu J., Duca G., Ahmed S., García Márquez F., Hajiyev A. (eds) Proceedings of the Fourteenth International Conference on Management Science and Engineering Management. ICMSEM 2020. Advances in Intelligent Systems and Computing, vol 1190. Springer, Cham. DOI:10.1007/978-3-030-49829-0_39. | ||||
Yildiz, MS (2018). Benford Yasasının Veri Doğruluğunun Değerlendirilmesi Amaçlı Kullanımı: Hastane Verileri İçin Bir Uygulama [Use of Benford's Law to Evaluate Data Accuracy: An Application for Hospital Data]. Yönetim ve Ekonomi 25(3), pp. 849-861. DOI:10.18657/yonveek.336919. TUR | ||||
Zago, JG (2021). Defense Methods for Convolutional Neural Networks Against Adversarial Attacks. Masters Thesis, Federal University of Santa Catarina, Florianópolis, Brazil. | ||||
Zago, JG, Baldissera, FL, Antonelo, EA and Saad, RT (2021). Benford’s law: what does it say on adversarial images?. Preprint arXiv:2102.04615 [cs.CV]; last accessed February 21, 2021. |