This work is cited by the following items of the Benford Online Bibliography:

Alves, AD, Yanasee, HH and Soma, NY (2016). An analysis of bibliometric indicators to JCR according to Benford’s law. Scientometrics 107(3), pp. 1489–1499. DOI:10.1007/s11192-016-1908-3. | ||||

Ausloos, M, Castellano, R and Cerqueti, R (2016). Regularities and discrepancies of credit default swaps: a data science approach through Benford's law. Chaos, Solitons & Fractals 90, pp. 8-17. DOI:10.1016/j.chaos.2016.03.002. | ||||

Ausloos, M, Cerqueti, R and Lupi, C (2017). Long-range properties and data validity for hydrogeological time series: The case of the Paglia river. Physica A: Statistical Mechanics and its Applications 470, pp. 39-50. DOI:10.1016/j.physa.2016.11.137. | ||||

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. | ||||

Bormashenko, E, Shulzinger, E, Whyman, G and Bormashenko, Y (2016). Benford’s law, its applicability and breakdown in the IR spectra of polymers. Physica A 444, pp. 524–529. DOI:10.1016/j.physa.2015.10.090. | ||||

Branets, S (2019). Detecting money laundering with Benford’s law and machine learning . Masters Thesis, University of Tartu. | ||||

González, F (2019). Detecting Anomalous Data in Household Surveys: Evidence for Argentina. Journal of Social and Economic Statistics 8(2), pp. 1-10. DOI:10.2478/jses-2019-0001. | ||||

González, F (2020). Self-reported income data: are people telling the truth?. To appear in Journal of Financial Crime. DOI:10.1108/JFC-08-2019-0113. | ||||

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. | ||||

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

Mir, TA (2016). The leading digit distribution of the worldwide illicit financial flows. Quality & Quantity vol. 50, p. 271-281. DOI:10.1007/s11135-014-0147-z. | ||||

Mir, TA and Ausloos, M (2018). Benford's law: a 'sleeping beauty' sleeping in the dirty pages of logarithmic tables. Journal of the Association for Information Science and Technology 69(3) pp. 349–358. DOI:10.1002/asi.23845. | ||||

Pavlović, V, Knežević, G, Joksimović, M and Joksimović, D (2019). Fraud Detection in Financial Statements Applying Benford's Law with Monte Carlo Simulation. Acta oeconomica 69(2), pp.217-239. DOI:10.1556/032.2019.69.2.4. | ||||

Riccioni, J and Cerqueti, R (2018). Regular paths in financial markets: Investigating the Benford’s law. Chaos, Solitons and Fractals 107, pp. 186-194. DOI:10.1016/j.chaos.2018.01.008. | ||||

Shi, J, Ausloos, M and Zhu, T (2018). Benford's law is the first significant digit and distribution distances for testing the reliability of financial reports in developing countries. Physica A: Statistical Mechanics and its Applications 492(1), pp. 878-888. DOI:10.1016/j.physa.2017.11.017. | ||||

Verkade, T (2015). Wat het cijfer 1 allemaal over ons prijsgeeft. Posted on De Correspondent June 16, 2015; last accessed March 24, 2016. DUT | ||||

Whyman, G, Ohtori, N, Shulzinger, E and Bormashenko, E (2016). Revisiting the Benford law: When the Benford-like distribution of leading digits in sets of numerical data is expectable?. Physica A: Statistical Mechanics and its Applications Volume 461, pp. 595-601. DOI:10.1016/j.physa.2016.06.054. | ||||

Whyman, G, Shulzinger, E and Bormashenko, E (2016). Intuitive considerations clarifying the origin and applicability of the Benford law. Results in Physics Volume 6, pp. 3-6 . DOI:10.1016/j.rinp.2015.11.010. |