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原文連結
論文資訊
- 類型：已發表論文
- 日期：2014-03-01
摘要
Many man-made and natural phenomena, including the intensity of earthquakes, 族群 of cities and size of international wars, are believed to follow power-law distributions. The accurate identification of power-law patterns has significant consequences for correctly understanding and modeling 複雜系統s. However, 統計 evidence for or against the power-law hypothesis is complicated by large fluctuations in the empirical distribution's tail, and these are worsened when 資訊 is lost from binning the data. We adapt the 統計ly principled framework for testing the power-law hypothesis, developed by Clauset, Shalizi and Newman, to the case of binned data. This approach includes maximum-likelihood fitting, a hypothesis test based on the Kolmogorov-Smirnov goodness-of-fit statistic and likelihood ratio tests for
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