Mathematics > Probability
[Submitted on 22 Sep 2013 (v1), last revised 9 Mar 2018 (this version, v4)]
Title:Benford's Law and Continuous Dependent Random Variables
View PDFAbstract:Many mathematical, man-made and natural systems exhibit a leading-digit bias, where a first digit (base 10) of 1 occurs not 11\% of the time, as one would expect if all digits were equally likely, but rather 30\%. This phenomenon is known as Benford's Law. Analyzing which datasets adhere to Benford's Law and how quickly Benford behavior sets in are the two most important problems in the field. Most previous work studied systems of independent random variables, and relied on the independence in their analyses.
Inspired by natural processes such as particle decay, we study the dependent random variables that emerge from models of decomposition of conserved quantities. We prove that in many instances the distribution of lengths of the resulting pieces converges to Benford behavior as the number of divisions grow, and give several conjectures for other fragmentation processes. The main difficulty is that the resulting random variables are dependent. We handle this by using tools from Fourier analysis and irrationality exponents to obtain quantified convergence rates as well as introducing and developing techniques to measure and control the dependencies. The construction of these tools is one of the major motivations of this work, as our approach can be applied to many other dependent systems. As an example, we show that the $n!$ entries in the determinant expansions of $n\times n$ matrices with entries independently drawn from nice random variables converges to Benford's Law.
Submission history
From: Steven Miller [view email][v1] Sun, 22 Sep 2013 14:17:53 UTC (240 KB)
[v2] Fri, 27 Dec 2013 15:05:43 UTC (243 KB)
[v3] Fri, 11 Aug 2017 08:28:14 UTC (397 KB)
[v4] Fri, 9 Mar 2018 11:41:57 UTC (367 KB)
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