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Problems with fitting to the power-law distribution


  • M. Goldstein
  • S. Morris
  • G. Yen



This short communication uses a simple experiment to show that fitting to a power law distribution by using graphical methods based on linear fit on the log-log scale is biased and inaccurate. It shows that using maximum likelihood estimation (MLE) is far more robust. Finally, it presents a new table for performing the Kolmogorov-Smirnov test for goodness-of-fit tailored to power-law distributions in which the power-law exponent is estimated using MLE. The techniques presented here will advance the application of complex network theory by allowing reliable estimation of power-law models from data and further allowing quantitative assessment of goodness-of-fit of proposed power-law models to empirical data. Copyright Springer-Verlag Berlin/Heidelberg 2004

Suggested Citation

  • M. Goldstein & S. Morris & G. Yen, 2004. "Problems with fitting to the power-law distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 41(2), pages 255-258, September.
  • Handle: RePEc:spr:eurphb:v:41:y:2004:i:2:p:255-258
    DOI: 10.1140/epjb/e2004-00316-5

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    References listed on IDEAS

    1. Pawe{l} Fiedor, 2013. "Frequency Effects on Predictability of Stock Returns," Papers 1310.5540,, revised Nov 2013.
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