A Graph-Prediction-Based Approach for Debiasing Underreported Data

TL;DR

This paper introduces a graph-based debiasing algorithm that jointly estimates event counts and discovery probabilities in underreported data, effectively addressing issues in fields like policing and pandemic data analysis without relying on strong priors.

Contribution

The paper proposes a novel graph-based algorithm for debiasing underreported data that jointly estimates counts and discovery probabilities without strong priors.

Findings

Effective in simulation experiments

Successfully applied to police 911 call data

Improves accuracy of underreporting correction

Abstract

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.