你的数据真的是帕累托分布的吗？
Are your data really Pareto distributed?
摘要：
帕累托分布和一般的幂律，已经证明是非常有用的模型来描述从物理到金融的非常不同的现象。近年来，经济物理学文献提出了大量的论文和模型来证明经济数据中幂律的存在。大多数时候，这种帕累托性是从一些图的观察中推断出来的，比如齐普夫图和平均超额图。如果Zipf图看起来几乎是线性的，那么一切正常，帕累托分布的参数被估计出来了。经常与OLS。不幸的是，正如我们在本文中所示，这些启发式图形工具是不可靠的。更确切地说，我们表明，只有一个组合的情节可以给一定程度的信心，真正存在的Paretianity的数据。我们首先回顾一些最重要的情节，讨论它们的优点和缺点，然后我们提出一些可用于改进分析的其他工具。
英文摘要：
Pareto distributions, and power laws in general, have demonstrated to be very useful models to describe very different phenomena, from physics to finance. In recent years, the econophysical literature has proposed a large amount of papers and models justifying the presence of power laws in economic data. Most of the times, this Paretianity is inferred from the observation of some plots, such as the Zipf plot and the mean excess plot. If the Zipf plot looks almost linear, then everything is ok and the parameters of the Pareto distribution are estimated. Often with OLS. Unfortunately, as we show in this paper, these heuristic graphical tools are not reliable. To be more exact, we show that only a combination of plots can give some degree of confidence about the real presence of Paretianity in the data. We start by reviewing some of the most important plots, discussing their points of strength and weakness, and then we propose some additional tools that can be used to refine the analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
