The contrast of fraud in international trade is a crucial task of modern economic regulations. We develop statistical tools for the detection of frauds in customs declarations that rely on the Newcomb-Benford law for significant digits. Our first contribution is to show the features, in the context of a European Union market, of the traders for which the law should hold in the absence of fraudulent data manipulation. Our results shed light on a relevant and debated question, since no general known theory can exactly predict validity of the law for genuine empirical data. We also provide approximations to the distribution of test statistics when the Newcomb-Benford law does not hold. These approximations open the door to the development of modified goodness-of-fit procedures with wide applicability and good inferential properties.
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http://dx.doi.org/10.1073/pnas.1806617115 | DOI Listing |
J Public Health (Oxf)
August 2024
Mathematics Division, University of Mindanao Digos College, Digos City 8002, Philippines.
Background: Public health surveillance is vital for monitoring and controlling disease spread. In the Philippines, an effective surveillance system is crucial for managing diverse infectious diseases. The Newcomb-Benford Law (NBL) is a statistical tool known for anomaly detection in various datasets, including those in public health.
View Article and Find Full Text PDFEntropy (Basel)
October 2022
Faculty of Natural Sciences, University of Ulm, Einsteinallee 11, D-89069 Ulm, Germany.
Related to the letters of an alphabet, entropy means the average number of binary digits required for the transmission of one character. Checking tables of statistical data, one finds that, in the first position of the numbers, the digits 1 to 9 occur with different frequencies. Correspondingly, from these probabilities, a value for the Shannon entropy H can be determined as well.
View Article and Find Full Text PDFGlobal Health
December 2022
Department of Political Science, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil.
Background: Claims of inconsistency in epidemiological data have emerged for both developed and developing countries during the COVID-19 pandemic.
Methods: In this paper, we apply first-digit Newcomb-Benford Law (NBL) and Kullback-Leibler Divergence (KLD) to evaluate COVID-19 records reliability in all 20 Latin American countries. We replicate country-level aggregate information from Our World in Data.
Mayo Clin Proc
February 2022
Harrington Heart and Vascular Institute, University Hospitals School of Medicine, Case Western Reserve University Cleveland, OH.
Sci Rep
November 2021
New Jersey City University, Jersey City, NJ, 07305, USA.
The COVID-19 pandemic has spurred controversies related to whether countries manipulate reported data for political gains. We study the association between accuracy of reported COVID-19 data and developmental indicators. We use the Newcomb-Benford law (NBL) to gauge data accuracy.
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