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Durst, RF, Huynh, C, Lott, A, Miller, SJ, Palsson, EA, Touw, W and Vreind, G (2016)

The Inverse Gamma Distribution and Benford's Law

Preprint in arXiv:1609.04106 [math.PR]; last accessed October 23, 2018.

ISSN/ISBN: Not available at this time. DOI: Not available at this time.

Abstract: According to Benford’s Law, many data sets have a bias towards lower leading digits (about 30% are 1’s). The applications of Benford’s Law vary: from detecting tax, voter and image fraud to determining the possibility of match-fixing in competitive sports. There are many common distributions that exhibit such bias, i.e. they are almost Benford. These include the exponential and the Weibull distributions. Motivated by these examples and the fact that the underlying distribution of factors in protein structure follows an inverse gamma distribution, we determine the closeness of this distribution to a Benford distribution as its parameters change.

@ARTICLE{2016arXiv160904106D, author = {{Durst}, Rebecca~F. and {Huynh}, Chi and {Lott}, Adam and {Miller}, Steven~J. and {Palsson}, Eyvindur~A. and {Touw}, Wouter and {Vriend}, Gert}, title = {The Inverse Gamma Distribution and Benford's Law}, journal = {ArXiv e-prints}, archivePrefix = "arXiv", eprint = {1609.04106}, primaryClass = {math.PR}, year = {2016}, month = {sep}, }

Reference Type: Preprint

Subject Area(s): Biology, Probability Theory