Nonparametric predictive inference for grouped lifetime data

This paper presents the application of a recently introduced nonparametric predictive inferential method to grouped lifetime data. Such data consist of numbers of events and numbers of right-censorings per interval, for a finite partition of the time-axis, without further information on the exact event and censoring times. Optimal bounds are derived for probabilities of events in terms of a future observation, where the bounds correspond to specific configurations of the event and censoring times per interval. The method is illustrated, and briefly compared to some alternative nonparametric approaches, via an example.