评估纽科姆-本福德定律(NBL)偏差的多重一致性检验:对 Koch 和 Okamura(2020 年)的复制

Dalson Figueiredo, Lucas Silva
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引用次数: 0

摘要

在本文中,我们对 Koch 和 Okamura(2020 年)关于中国 COVID-19 数据符合本福德定律的结论进行了批判性的重新评估。在 Figueiredo 等人(2022 年)的基础上,我们采用了一个结合多种检验方法的框架,包括 Chi-square、Kolmogorov-Smirnov、Euclidean Distance、Mean Absolute Deviation、Distortion Factor 和 Mantissa Distribution。采用多重检验的主要原因是为了增强推论的稳健性。研究的主要发现表明,中国的 COVID-19 感染并不符合本福德定律的预期分布,也不符合在美国和意大利观察到的数据。偏离本福德定律的数据是否有助于发现误报或欺诈性数据仍存在争议。然而,要解决这一问题,需要进行比 Koch 和 Okamura(2020 年)论文更细致的统计分析。通过结合使用完全透明的程序进行多项测试,我们建立了一种更可靠的方法来评估应用研究是否符合纽科姆-本福德定律。
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Multiple conformity tests to assess deviations from the Newcomb-Benford Law (NBL): A replication of Koch and Okamura (2020)
In this paper, we critically reevaluate Koch and Okamura’s (2020) conclusions on the conformity of Chinese COVID-19 data with Benford’s Law. Building on Figueiredo et al. (2022), we adopt a framework that combines multiple tests, including Chi-square, Kolmogorov-Smirnov, Euclidean Distance, Mean Absolute Deviation, Distortion Factor, and Mantissa Distribution. The primary rationale behind employing multiple tests is to enhance the robustness of our inference. The main finding of the study indicates that COVID-19 infections in China do not adhere to the distribution expected under Benford’s Law, nor does it align with the figures observed in the U.S. and Italy. The usefulness of deviations from Benford’s Law in detecting misreported or fraudulent data remains controversial. However, addressing this question requires a more careful statistical analysis than what is presented in the Koch and Okamura (2020) paper. By employing a combination of several tests using fully transparent procedures, we establish a more reliable approach to evaluating conformity to the Newcomb-Benford Law in applied research.
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来源期刊
Model Assisted Statistics and Applications
Model Assisted Statistics and Applications Mathematics-Applied Mathematics
CiteScore
1.00
自引率
0.00%
发文量
26
期刊介绍: Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.
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