Abstract
Summary The paper presents a Bayesian approach to analysing interval-censored data with random unknown end points. Such data occur when the event of interest is interval censored but, because of the measurement process, the interval end points are not known exactly. Modelling the measurement process permits inference that accounts for this source of variability. Our results are motivated by an experimental study that was designed to characterize the cosmic-ray–neutron-induced soft error rate of a semiconductor device.
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