Nonparametric Predictive Inference for Discrete Lifetime Data

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

Abstract

This paper presents nonparametric predictive inference for discrete lifetime data. While lifetimes are mostly treated as continuous random variables in statistics, there are scenarios where time observations are recorded as discrete values, for example, in actuary, where lifetimes are often recorded as integers in years. The presented method provides lower and upper probabilities for a variety of events of interest involving discrete lifetimes, with examples provided for illustration. Furthermore, the discrete-time situation is considered for inference of the reliability of systems, with discrete-time data for components of different types and using the survival signature to combine inference on components’ reliability to quantify the overall system reliability.

Citation

Coolen, F. P. A., Coolen-Maturi, T., & Mahnashi, A. M. Y. (2024). Nonparametric Predictive Inference for Discrete Lifetime Data. Mathematics, 12(22), Article 3514. https://doi.org/10.3390/math12223514

• Journal Article Type: Article
• Acceptance Date: Oct 28, 2024
• Online Publication Date: Nov 11, 2024
• Publication Date: Nov 2, 2024
• Deposit Date: Nov 3, 2024
• Publicly Available Date: Nov 22, 2024
• Journal: Mathematics
• Electronic ISSN: 2227-7390
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 22
• Article Number: 3514
• DOI: https://doi.org/10.3390/math12223514
• Keywords: 62G99, survival signature, nonparametric predictive inference, discrete lifetime data
• Public URL: https://durham-repository.worktribe.com/output/3080541