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低估数据去偏算法（GRAUD），旨在高效联合估计空间或图结构中的事件计数与发现概率。该创新方法解决了诸如警务数据与COVID-19数据分析等领域中的难题，且无需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的图结构，我们的方法能够同时对低估数据进行去偏并估计发现概率。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——警方911服务呼叫数据中阐释了其实用性。