We present a novel Graph-based debiasing Algorithm for Underreported Data (GRAUD) aiming at an efficient joint estimation of event counts and discovery probabilities across spatial or graphical structures. This innovative method provides a solution to problems seen in fields such as policing data and COVID-$19$ data analysis. Our approach avoids the need for strong priors typically associated with Bayesian frameworks. By leveraging the graph structures on unknown variables $n$ and $p$, our method debiases the under-report data and estimates the discovery probability at the same time. We validate the effectiveness of our method through simulation experiments and illustrate its practicality in one real-world application: police 911 calls-to-service data.

翻译：我们提出了一种新颖的基于图的欠报告数据去偏算法（GRAUD），旨在高效联合估计空间或图结构上的事件计数与发现概率。该方法为警务数据、COVID-19数据分析等领域中常见的问题提供了解决方案，避免了贝叶斯框架通常所需的强先验假设。通过利用未知变量$n$和$p$上的图结构，我们的方法在去偏欠报告数据的同时，同步估计发现概率。我们通过仿真实验验证了方法的有效性，并在一个真实应用场景——911报警服务数据中展示了其实用性。