电子邮件通信中的泊松爆发
Poissonian bursts in e-mail correspondence
摘要：
最近的工作表明，电子邮件通信的事件间时间的分布呈现出一个沉重的尾巴，这是统计上一致的级联泊松过程。在这项工作中，我们将分析扩展到高阶统计，使用Fano和Allan因子来量化经验数据偏离泊松统计的已知相关性的程度。分析表明，高阶统计的经验数据是无法区分的随机重新排序的时间序列，从而表明，电子邮件通信是没有更多的突发或相关的泊松过程。此外，由级联泊松过程生成的合成数据集复制了在经验数据中观察到的突发性和相关性。最后，一个简单的重新标度分析使用的最佳估计率的活动，证实了经验观察到的相关性产生的非高斯泊松过程。
英文摘要：
Recent work has shown that the distribution of inter-event times for e-mail communication exhibits a heavy tail which is statistically consistent with a cascading Poisson process. In this work we extend the analysis to higher-order statistics, using the Fano and Allan factors to quantify the extent to which the empirical data depart from the known correlations of Poissonian statistics. The analysis shows that the higher-order statistics from the empirical data is indistinguishable from that of randomly reordered time series, thus demonstrating that e-mail correspondence is no more bursty or correlated than a Poisson process. Furthermore synthetic data sets generated by a cascading Poisson process replicate the burstiness and correlations observed in the empirical data. Finally, a simple rescaling analysis using the best-estimate rate of activity, confirms that the empirically observed correlations arise from a non-homogeneus Poisson process.
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