This work is cited by the following items of the Benford Online Bibliography:
Ausloos, M, Cerqueti, R and Mir, TA (2017). Data science for assessing possible tax income manipulation: The case of Italy. Chaos, Solitons and Fractals 104, pp. 238–256. DOI:10.1016/j.chaos.2017.08.012. | ||||
Burns, B (2009). Sensitivity to statistical regularities: People (largely) follow Benford’s law. pp 2872-2877 in: Proceedings of CogSci 2009, Amsterdam, The Netherlands. | ||||
Dlugosz, S and Müller-Funk, U (2009). The value of the last digit: statistical fraud detection with digit analysis. Advances in Data Analysis and Classification 3, 281-290. | ||||
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. | ||||
Graham, SDJ, Hasseldine, J and Paton, D (2009). Statistical fraud detection in a commercial lobster fishery. New Zealand Journal of Marine and Freshwater Research Volume 43, Issue 1, 2009, pages 457-463. | ||||
Haferkorn, M (2013). Humans vs. Algorithms – Who Follows Newcomb-Benford’s Law Better with Their Order Volume?. Enterprise Applications and Services in the Finance Industry: Lecture Notes in Business Information Processing Volume 135, pp 61-70 . ISSN/ISBN:9783642362187. DOI:10.1007/978-3-642-36219-4_4. | ||||
Hein, J, Zobrist, R, Konrad, C and Schuepfer, G (2012). Scientific fraud in 20 falsified anesthesia papers : detection using financial auditing methods. Der Anaesthesist 61(6), pp. 543-9. DOI:10.1007/s00101-012-2029-x. | ||||
Huang, SM, Yen, DC, Yang, LW and Hua, JS (2008). An investigation of Zipf's Law for fraud detection. Decision Support Systems 46(1), pp. 70-83. DOI:10.1016/j.dss.2008.05.003. | ||||
Kundt, TC (2014). Applying "Benford's law" to the Crosswise Model: Findings from an online survey on tax evasion . Helmut-Schnidt-University, Department of Economics, Working Paper, 148/2014. | ||||
Lu, F (2007). Uncovering Fraud in Direct Marketing Data with a Fraud Auditing Case Builder. Lecture Notes in Computer Science 4702, pp. 540-547. ISSN/ISBN:978-3-540-74975-2. DOI:10.1007/978-3-540-74976-9_56. | ||||
Lu, F and Boritz, JE (2005). Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford’s Law Distributions. Machine Learning: ECML 2005 (Proceedings). Lecture Notes in Artificial Intelligence 3270, pp. 633-640. ISSN/ISBN:0302-9743. | ||||
Lu, F, Boritz, JE and Covvey, D (2006). Adaptive Fraud Detection Using Benford’s Law. Advances in Artificial Intelligence Lecture Notes in Computer Science Volume 4013, pp. 347-358. ISSN/ISBN:978-3-540-34628-9. DOI:10.1007/11766247_30. | ||||
Miller, SJ (ed.) (2015). Benford's Law: Theory and Applications. Princeton University Press: Princeton and Oxford. ISSN/ISBN:978-0-691-14761-1. | ||||
Nigrini, MJ (2011). Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations. John Wiley & Sons: Hoboken, New Jersey. ISSN/ISBN:978-0-470-89046-2. | ||||
Otey, ME (2006). Approaches to Abnormality Detection with Constraints. PhD thesis, The Ohio State University, USA. | ||||
Schüpfer, G, Hein, J, Casutt, M, Steiner, L and Konrad, C (2012). Vom Finanz- sum Wissenschaftsbetrug [From financial to scientific fraud : methods to detect discrepancies in the medical literature]. Der Anaesthesist 61(6):537-42. ISSN/ISBN:0003-2417. DOI:10.1007/s00101-012-2028-y. GER | ||||
Suh, I and Headrick, TC (2010). A comparative analysis of the bootstrap versus traditional statistical procedures applied to digital analysis based on Benford's Law. Journal of Forensic and Investigative Accounting 2(2), 2010, 144-175. | ||||
Tsagbey, S, de Carvalho, M and Page, GL (2017). All Data are Wrong, but Some are Useful? Advocating the Need for Data Auditing . The American Statistician, 71, pp. 231--235. DOI:10.1080/00031305.2017.1311282. | ||||
Tsung, F, Zhou, Z and Jiang, W (2007). Applying manufacturing batch techniques to fraud detection with incomplete customer information. IIE Transactions 39(6), 671-680. DOI:10.1080/07408170600897510. |