Campus Units

Statistics

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

1-14-2021

Journal or Book Title

Journal of the American Statistical Association

DOI

10.1080/01621459.2020.1850461

Abstract

This paper 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 paper 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-distributionbased methods that perform better than the calibration method are presented and justified.

Comments

This is a manuscript of an article published as Tian, Qinglong, Fanqi Meng, Daniel J. Nordman, and William Q. Meeker. "Predicting the number of future events." Journal of the American Statistical Association (2021). doi:10.1080/01621459.2020.1850461. Posted with permission.

Copyright Owner

American Statistical Association

Language

en

File Format

application/pdf

Published Version

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