Specifying Prior Distributions in Reliability Applications

Date
2022-04-12
Authors
Tian, Qinglong
Lewis-Beck, Colin
Niemi, Jarad
Meeker, William
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
© 2022 The Authors
Altmetrics
Authors
Research Projects
Organizational Units
Statistics
Organizational Unit
Journal Issue
Series
Department
Statistics
Abstract
Especially when facing reliability data with limited information (e.g., a small number of failures), there are strong motivations for using Bayesian inference methods. These include the option to use information from physics-of-failure or previous experience with a failure mode in a particular material to specify an informative prior distribution. Another advantage is the ability to make statistical inferences without having to rely on specious (when the number of failures is small) asymptotic theory needed to justify non-Bayesian methods. Users of non-Bayesian methods are faced with multiple methods of constructing uncertainty intervals (Wald, likelihood, and various bootstrap methods) that can give substantially different answers when there is little information in the data. For Bayesian inference, there is only one method -- but it is necessary to provide a prior distribution to fully specify the model. Much work has been done to find default or objective prior distributions that will provide inference methods with good (and in some cases exact) frequentist coverage properties. This paper reviews some of this work and provides, evaluates, and illustrates principled extensions and adaptations of these methods to the practical realities of reliability data (e.g., non-trivial censoring).
Comments
This preprint is made available through arXiv at doi:https://doi.org/10.48550/arXiv.2204.06099.
Description
Keywords
Citation
DOI
Collections