Predicting the Number of Future Events

This article describes prediction methods for the number of future events from a population of units associated with an on-going time-to-event process. Examples include the prediction of warranty returns and the prediction of the number of future product failures that could cause serious threats to property or life. Important decisions such as whether a product recall should be mandated are often based on such predictions. Data, generally right-censored (and sometimes left truncated and right-censored), are used to estimate the parameters of a time-to-event distribution. This distribution can then be used to predict the number of events over future periods of time. Such predictions are sometimes called within-sample predictions and differ from other prediction problems considered in most of the prediction literature. This article shows that the plug-in (also known as estimative or naive) prediction method is not asymptotically correct (i.e., for large amounts of data, the coverage probability always fails to converge to the nominal confidence level). However, a commonly used prediction calibration method is shown to be asymptotically correct for within-sample predictions, and two alternative predictive-distribution-based methods that perform better than the calibration method are presented and justified. for this article are available online.

Automated summary

This article discusses methods for predicting the number of future events from a population of units related to an ongoing time-to-event process. It highlights the importance of using right-censored data to estimate parameters and predict future events. The article reveals that the plug-in prediction method is not asymptotically correct for large amounts of data, while a commonly used prediction calibration method is shown to be correct for within-sample predictions. It also introduces two alternative predictive-distribution-based methods that outperform the calibration method.