General distributions of number representation elements

IF 0.7 3区 工程技术 Q4 ENGINEERING, INDUSTRIAL Probability in the Engineering and Informational Sciences Pub Date : 2024-02-07 DOI:10.1017/s0269964823000207
Félix Balado, Guénolé C. M. Silvestre
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Abstract

We provide general expressions for the joint distributions of the k most significant b-ary digits and of the k leading continued fraction (CF) coefficients of outcomes of arbitrary continuous random variables. Our analysis highlights the connections between the two problems. In particular, we give the general convergence law of the distribution of the jth significant digit, which is the counterpart of the general convergence law of the distribution of the jth CF coefficient (Gauss-Kuz’min law). We also particularise our general results for Benford and Pareto random variables. The former particularisation allows us to show the central role played by Benford variables in the asymptotics of the general expressions, among several other results, including the analogue of Benford’s law for CFs. The particularisation for Pareto variables—which include Benford variables as a special case—is especially relevant in the context of pervasive scale-invariant phenomena, where Pareto variables occur much more frequently than Benford variables. This suggests that the Pareto expressions that we produce have wider applicability than their Benford counterparts in modelling most significant digits and leading CF coefficients of real data. Our results may find practical application in all areas where Benford’s law has been previously used.
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数字表示元素的一般分布
我们提供了任意连续随机变量结果的 k 个最重要 bary 数字和 k 个前导连续分数 (CF) 系数联合分布的一般表达式。我们的分析强调了这两个问题之间的联系。特别是,我们给出了第 j 个有效数字分布的一般收敛规律,它与第 j 个 CF 系数分布的一般收敛规律(高斯-库兹明规律)相对应。我们还对本福德随机变量和帕累托随机变量的一般结果进行了特殊化。前者的特殊化使我们能够展示本福德变量在一般表达式渐近中的核心作用,以及其他一些结果,包括 CF 的本福德定律类似物。帕累托变量的特殊化--其中包括作为特例的本福德变量--与普遍的规模不变现象尤其相关,因为帕累托变量比本福德变量出现得更频繁。这表明,在模拟真实数据的最显著位数和前导 CF 系数时,我们得出的帕累托表达式比其对应的本福德表达式具有更广泛的适用性。我们的结果可以实际应用于以前使用本福德定律的所有领域。
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来源期刊
CiteScore
2.20
自引率
18.20%
发文量
45
审稿时长
>12 weeks
期刊介绍: The primary focus of the journal is on stochastic modelling in the physical and engineering sciences, with particular emphasis on queueing theory, reliability theory, inventory theory, simulation, mathematical finance and probabilistic networks and graphs. Papers on analytic properties and related disciplines are also considered, as well as more general papers on applied and computational probability, if appropriate. Readers include academics working in statistics, operations research, computer science, engineering, management science and physical sciences as well as industrial practitioners engaged in telecommunications, computer science, financial engineering, operations research and management science.
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