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Technological improvements or climate change? Bayesian modeling of time-varying conformance to Benford’s Law

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  • Junho Lee
  • Miguel de Carvalho

Abstract

We develop a Bayesian time-varying model that tracks periods at which conformance to Benford’s Law is lower. Our methods are motivated by recent attempts to assess how the quality and homogeneity of large datasets may change over time by using the First-Digit Rule. We resort to a smooth multinomial logistic model which captures the dynamics governing the proportion of first digits, and apply the proposed model to global tropical cyclone tracks over the past two centuries. Our findings indicate that cumulative technological improvements may have only had a moderate influence on the homogeneity of the dataset, and hint that recent heterogeneity could be due to other drivers.

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  • Junho Lee & Miguel de Carvalho, 2019. "Technological improvements or climate change? Bayesian modeling of time-varying conformance to Benford’s Law," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-11, April.
    • Handle: RePEc:plo:pone00:0213300
    DOI: 10.1371/journal.pone.0213300
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    References listed on IDEAS

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    1. Sitsofe Tsagbey & Miguel de Carvalho & Garritt L. Page, 2017. "All Data are Wrong, but Some are Useful? Advocating the Need for Data Auditing," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 231-235, July.
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