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发生次数与发现概率。该创新方法为警务数据和COVID-19数据分析等领域中的问题提供了解决方案。我们的方法避免了贝叶斯框架中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，该方法能够在去偏欠报告数据的同时估计发现概率。我们通过仿真实验验证了该方法的有效性，并在一个实际应用案例——警方911服务请求数据——中展示了其实用性。