论文标题
从次指定分布到黑天鹅优势
From subexponential distributions to black swan dominance
论文作者
论文摘要
用重尾巴的经验分布的形状是辩论的经常性问题。有权力法和相关规模不变性的主张。还有很多挑战者，木制正常和伸展指数等。在这里，我指出，关于求和不变性，重要的是它们是亚指数分布。我提供的数值示例突出了亚指数分布的关键属性。总和不变性和黑天鹅的优势：总和由最大值主导。最后，我说明了这些属性在随机网络，传染性动态和项目延迟中解决问题的使用。
The shape of empirical distributions with heavy tails is a recurrent matter of debate. There are claims of a power laws and the associated scale invariance. There are plenty of challengers as well, the lognormal and stretched exponential among others. Here I point out that, with regard to summation invariance, all what matters is they are subexponential distributions. I provide numerical examples highlighting the key properties of subexponential distributions. The summation invariance and the black swan dominance: the sum is dominated by the maximum. Finally, I illustrate the use of these properties to tackle problems in random networks, infectious dynamics and project delays.
