Nonparametric Predictive Inference for Discrete Lifetime Data

Frank P. A. Coolen (), Tahani Coolen-Maturi and Ali M. Y. Mahnashi
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Frank P. A. Coolen: Department of Mathematical Sciences, Durham University, Durham DH1 3LE, UK
Tahani Coolen-Maturi: Department of Mathematical Sciences, Durham University, Durham DH1 3LE, UK
Ali M. Y. Mahnashi: Department of Mathematics, College of Science, Jazan University, Jazan 45 142, Saudi Arabia

Mathematics, 2024, vol. 12, issue 22, 1-14

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.

Keywords: discrete lifetime data; nonparametric predictive inference; survival signature (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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