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Cho, WKT and Gaines, BJ (2007). Breaking the (Benford) law: Statistical fraud detection in campaign finance. American Statistician 61(3), pp. 218-223. ISSN/ISBN:0003-1305. DOI:10.1198/000313007X223496.
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Durtschi, C, Hillison, W and Pacini, C (2004). The effective use of Benford’s law to assist in detecting fraud in accounting data. Journal of Forensic Accounting 1524-5586/Vol. V, pp. 17-34.
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Jamain, A (2001). Benford’s Law. Master Thesis. Imperial College of London and ENSIMAG.
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Joannes-Boyau, R, Bodin, T, Scheffers, A, Sambridge, M and May, SM (2015). Using Benford’s law to investigate Natural Hazard dataset homogeneity. Nature -Scientific Reports 5:12046, pp. 1-8 . DOI:10.1038/srep12046.
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Kafri, O (2009). Entropy Principle in Direct Derivation of Benford's Law. posted on arXiv 8 March 2009 - arXiv:0901.3047v2.
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Miller, SJ (ed.) (2015). Benford's Law: Theory and Applications. Princeton University Press: Princeton and Oxford. ISSN/ISBN:978-0-691-14761-1.
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Mir, TA (2016). Citations to articles citing Benford's law: a Benford analysis. arXiv:1602.01205; posted Feb 3, 2016.
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Newcomb, S (1881). Note on the frequency of use of the different digits in natural numbers. American Journal of Mathematics 4(1), pp. 39-40. ISSN/ISBN:0002-9327. DOI:10.2307/2369148.
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Scott, PD and Fasli, M (2001). Benford’s law: an empirical investigation and a novel explanation. CSM Technical Report 349, Department of Computer Science, University of Essex, UK.
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Winter, C, Schneider, M and Yannikos, Y (2012). Model-Based Digit Analysis for Fraud Detection overcomes Limitations of Benford Analysis. Availability, Reliability and Security (ARES 2012), Seventh International Conference, August 20–24, 2012, Prague, Czech Republic.
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IEEE Computer Society. ISSN/ISBN:978-1-4673-2244-7 . DOI:10.1109/ARES.2012.37.
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