Skip to main content

Research Repository

Advanced Search

Nonparametric Predictive Inference for Two Future Observations with Right-Censored Data

Coolen-Maturi, Tahani; Mahnashi, Ali M; Coolen, Frank P A

Authors

Ali M Mahnashi



Abstract

In reliability and survival analyses, right-censored observations are common. This type of data occurs when an event of interest is not fully observed during an experiment and there is no information provided about a random quantity, except that it exceeds a certain value. Nonparametric Predictive Inference (NPI) is a frequentist statistical method that relies on only few assumptions. It quantifies uncertainty by using imprecise probabilities based on Hill's assumption A (n) and focuses specifically on future observations. NPI has been developed for various types of data, including right-censored data, for some inferences such as multiple group comparisons, uncertainty quantification of the survival function, and in the context of competing risks. However, NPI with right-censored data has only considered a single future observation. This paper aims to extend this method by considering two future observations and taking into account that in the NPI approach, such multiple future observations are not conditionally independent given the data. Specifically, we present NPI lower and upper probabilities for the event that both future observations are greater than a particular time. Examples are provided for illustration and an application to system reliability is presented.

Citation

Coolen-Maturi, T., Mahnashi, A. M., & Coolen, F. P. A. (in press). Nonparametric Predictive Inference for Two Future Observations with Right-Censored Data. Mathematical Methods of Statistics,

Journal Article Type Article
Acceptance Date May 26, 2024
Deposit Date May 28, 2024
Journal Mathematical Methods of Statistics
Print ISSN 1066-5307
Publisher Springer
Peer Reviewed Peer Reviewed
Keywords Nonparametric predictive inference; right-censored data; censoring; imprecise probability; future observations; system reliability
Public URL https://durham-repository.worktribe.com/output/2466464
Publisher URL https://link.springer.com/journal/12004