Benford, F (1938). The law of anomalous numbers. Proceedings of the American Philosophical Society, Vol. 78, No. 4 (Mar. 31, 1938), pp. 551572.





Cho, WKT and Gaines, BJ (2007). Breaking the (Benford) law: Statistical fraud detection in campaign finance. American Statistician 61(3), pp. 218223. ISSN/ISBN:00031305. DOI:10.1198/000313007X223496.





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 15245586/Vol. V, pp. 1734.





Jamain, A (2001). Benford’s Law. Master Thesis. Imperial College of London and ENSIMAG.





JoannesBoyau, 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. 18 . DOI:10.1038/srep12046.





Kafri, O (2009). Entropy Principle in Direct Derivation of Benford's Law. posted on arXiv 8 March 2009  arXiv:0901.3047v2.





Miller, SJ (ed.) (2015). Benford's Law: Theory and Applications. Princeton University Press: Princeton and Oxford. ISSN/ISBN:9780691147611.





Mir, TA (2016). Citations to articles citing Benford's law: a Benford analysis. arXiv:1602.01205; posted Feb 3, 2016.





Newcomb, S (1881). Note on the frequency of use of the different digits in natural numbers. American Journal of Mathematics 4(1), pp. 3940. ISSN/ISBN:00029327. DOI:10.2307/2369148.





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.





Winter, C, Schneider, M and Yannikos, Y (2012). ModelBased 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.
IEEE CS volume E4775, pages 255–261.
IEEE Computer Society. ISSN/ISBN:9781467322447 . DOI:10.1109/ARES.2012.37.




