arXiv:2510.04421v3 Announce Type: replace-cross Abstract: Survival analysis provides statistical methods to model the time until an event occurs. Reporting delays arise when event times are not observed at their occurrence but are only revealed upon reporting. This issue is particularly critical for timely risk evaluation when the observation window is short due to administrative censoring. In this study, we incorporate right-censored reporting delays by jointly modeling parametric hazards for the event and reporting processes. We then construct a consistent estimator for the model parameters
This research addresses a critical challenge in timely risk evaluation within medical and other time-sensitive applications, where reporting delays obscure event analysis.
Improved survival analysis models with right-censored reporting delays can lead to more accurate predictions and better-informed decisions in fields reliant on real-time data.
The ability to accurately model events despite reporting lags will enhance the reliability of risk assessments and decision-making processes in dynamic environments.
- · Healthcare sector
- · Insurance companies
- · Public health organizations
- · Data scientists
- · Organizations relying on simplistic delay models
- · Areas with unsophisticated data analysis pipelines
More precise forecasting of critical events given imperfect data streams.
Reduced operational risks and costs across various industries due to better predictive capabilities.
Potential for new AI applications in real-time risk management for rapidly evolving situations like pandemics or supply chain disruptions.
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Read at arXiv cs.LG