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      Given enough eyeballs, all bugs are shallow? Revisiting Eric Raymond with bug bounty programs

      1 , 2 , 3 , 4
      Journal of Cybersecurity
      Oxford University Press (OUP)

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          Is Open Access

          Power-law distributions in empirical data

          Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.
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            A Theory of Auctions and Competitive Bidding

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              Random difference equations and Renewal theory for products of random matrices

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                Author and article information

                Journal
                Journal of Cybersecurity
                Oxford University Press (OUP)
                2057-2085
                2057-2093
                June 2017
                June 01 2017
                October 13 2017
                June 2017
                June 01 2017
                October 13 2017
                : 3
                : 2
                : 81-90
                Affiliations
                [1 ]Graduate School of Economics and Management, University of Geneva, Campus Biotech, 9 chemin des Mines, 1202 Geneva, Switzerland;
                [2 ]Snap Inc., 63 Market St, Venice, CA 90291, USA;
                [3 ]Technical University of Munich, Department of Informatics, Chair for Cyber Trust, Boltzmannstrasse 3, 85748 Garching, Germany and
                [4 ]School of Information, University of California at Berkeley, 102 South Hall #4600, Berkeley, CA 94720-4600, USA
                Article
                10.1093/cybsec/tyx008
                74856316-fc9a-46e8-81f1-491538cddba7
                © 2017
                History

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