Department seminar by Qinglong Tian

Wednesday, January 26, 2022 10:00 am - 10:00 am EST (GMT -05:00)

Please Note: This seminar will be given online.

Department seminar

Qinglong Tian
University of Wisconsin-Madison

Link to join seminar: Hosted on Zoom

Predicting the number of future events

This work describes prediction methods for the number of future events from a population of units associated with an ongoing 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 methods that perform better than the calibration method are presented and justified.