摘要翻译：
我报告了一个新的统计分布，以应对由高度倾斜和/或瘦尾（“胖尾或重尾”）数据提出的臭名昭著的、长期存在的计算/建模挑战。分配是直接、灵活和有效的。即使使用比常规要求少得多的数据点，它也在一个单一概率密度函数(PDF)的背景下，从峰值中心到远尾，对非高斯数据样本进行建模，该函数在非常广泛的色散和概率密度范围内有效。分布是一种精确的工具，可以用来描述厚尾数据所固有的巨大风险和巨大机会。
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英文标题：
《Fat Tails Quantified and Resolved: A New Distribution to Reveal and
  Characterize the Risk and Opportunity Inherent in Leptokurtic Data》
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作者：
Lawrence R. Thorne
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最新提交年份：
2011
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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英文摘要：
  I report a new statistical distribution formulated to confront the infamous, long-standing, computational/modeling challenge presented by highly skewed and/or leptokurtic ("fat- or heavy-tailed") data. The distribution is straightforward, flexible and effective. Even when working with far fewer data points than are routinely required, it models non-Gaussian data samples, from peak center through far tails, within the context of a single probability density function (PDF) that is valid over an extremely broad range of dispersions and probability densities. The distribution is a precision tool to characterize the great risk and the great opportunity inherent in fat-tailed data.
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PDF链接：
https://arxiv.org/pdf/1110.6553