2013/05/29 by Efstratios Rappos, Rappos, Efstratios, Stephan Robert +1
Computer Science · Mathematics · Physics and Astronomy · #62F03 #62P10 #62P30 #62P35 #62Q05 #68W10 #Applications (stat.AP) #Computation (stat.CO) #Computational Physics (physics.comp-ph) #Computational Physics and Python Applications #Data Analysis #Distributed #FOS: Computer and information sciences #FOS: Physical sciences #Model Reduction and Neural Networks #Parallel #Statistics and Probability (physics.data-an) #and Cluster Computing (cs.DC) #cs.DC #msc:62F03 #msc:62P10 #msc:62P30 #msc:62P35 #msc:62Q05 #msc:68W10 #physics.comp-ph #physics.data-an #stat.AP #stat.CO
paper · pdf · doi:10.48550/arxiv.1305.6738
arxiv created 2013/05/29 · openalex publication_date 2013/05/29 · arxiv updated 2013/05/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This article describes a methodology for fitting experimental data to the discrete power-law distribution and provides the results of a detailed simulation exercise used to calculate accurate cutoff values used to assess the fit to a power-law distribution when using the maximum likelihood estimation for the exponent of the distribution. Using massively parallel programming computing, we were able to accelerate by a factor of 60 the computational time required for these calculations across a range of parameters and construct a series of detailed tables containing the test values to be used in a Kolmogorov-Smirnov goodness-of-fit test, allowing for an accurate assessment of the power-law fit from empirical data.