Predicting the Number of Future Events

Abstract

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. Supplementary materials for this article are available online.

KEYWORDS:

• Binomial predictand
• Bootstrap
• Calibration
• Censored data
• Predictive distribution

Supplementary Materials

The online supplementary material provides proofs, extra simulation results, algorithms, and data.

Acknowledgments

We would like to thanks Luis A. Escobar for helpful comments on this article. We are also grateful to the editorial staff, including two reviewers, for helpful comments that improved the article.