Preprint on Researchgate.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: We show, by means of a numerical simulation, that the asymptotic (n ≥ 100) cumulative distribution function of the Chebyshev distance statistic is well approximated by a log-normal function with parameters μ = −0.6183 and σ = 0.3561 in the null hypothesis that Benford’s law holds. The deviations of the cumulative function observed in Monte Carlo simulations from the empirical one are below 0.5%. This makes the statistical test based on the Chebyshev statistic accurate at a level of 1% when testing Benford’s law for moderately large and large numbers of data points.
Bibtex:
@misc{,
author = {Leonardo Campanelli},
title = {A test of significance for Benford’s law based on the Chebyshev distance},
year = {2023},
url = {https://www.researchgate.net/profile/Leonardo-Campanelli-2/publication/377014152_A_test_of_significance_for_Benford%27s_law_based_on_the_Chebyshev_distance/links/6591e1e36f6e450f19ba4ada/A-test-of-significance-for-Benfords-law-based-on-the-Chebyshev-distance.pdf},
}
Reference Type: Preprint
Subject Area(s): Statistics