A Hierarchical Model for Heterogenous Reliability Field Data

dc.contributor.author Mittman, Eric
dc.contributor.author Lewis-Beck, Colin
dc.contributor.author Meeker, William
dc.contributor.author Meeker, William
dc.contributor.department Statistics
dc.date 2019-09-19T06:45:44.000
dc.date.accessioned 2020-07-02T06:56:41Z
dc.date.available 2020-07-02T06:56:41Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2019
dc.date.issued 2019-01-01
dc.description.abstract <p>When analyzing field data on consumer products, model-based approaches to inference require a model with sufficient flexibility to account for multiple kinds of failures. The causes of failure, while not interesting to the consumer per se, can lead to various observed lifetime distributions. Because of this, standard lifetime models, such as using a single Weibull or lognormal distribution, may be inadequate. Usually cause-of-failure information will not be available to the consumer and thus traditional competing risk analyses cannot be performed. Furthermore, when the information carried by lifetime data are limited by sample size, censoring, and truncation, estimates can be unstable and suffer from imprecision. These limitations are typical, for example, lifetime data for high-reliability products will naturally tend to be right-censored. In this article, we present a method for joint estimation of multiple lifetime distributions based on the generalized limited failure population (GLFP) model. This five-parameter model for lifetime data accommodates lifetime distributions with multiple failure modes: early failures (sometimes referred to in the literature as “infant mortality”) and failures due to wearout. We fit the GLFP model to a heterogenous population of devices using a hierarchical modeling approach. Borrowing strength across subpopulations, our method enables estimation with uncertainty of lifetime distributions even in cases where the number of model parameters is larger than the number of observed failures. Moreover, using this Bayesian method, comparison of different product brands across the heterogenous population is straightforward because estimation of arbitrary functionals is easy using draws from the joint posterior distribution of the model parameters. Potential applications include assessment and comparison of reliability to inform purchasing decisions. Supplementary materials for this article are available online.</p>
dc.description.comments <p>This is the Submitted Manuscript of an article published by Taylor & Francis as Mittman, Eric, Colin Lewis-Beck, and William Q. Meeker. "A Hierarchical Model for Heterogenous Reliability Field Data." <em>Technometrics</em> 61, no. 3 (2019). DOI: <a href="http://dx.doi.org/10.1080/00401706.2018.1518273" target="_blank">10.1080/00401706.2018.1518273</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/125/
dc.identifier.articleid 1127
dc.identifier.contextkey 11061279
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/125
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90427
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/125/hier_glfp.pdf|||Tue Jan 16 02:21:06 UTC 2018
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/125/hierarhicalreliability.pdf|||Fri Jan 14 19:23:12 UTC 2022
dc.source.uri 10.1080/00401706.2018.1518273
dc.subject.disciplines Statistics and Probability
dc.subject.keywords Bathtub hazard
dc.subject.keywords Bayesian estimation
dc.subject.keywords Censored data
dc.subject.keywords GLFP
dc.subject.keywords Stan
dc.title A Hierarchical Model for Heterogenous Reliability Field Data
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication a1ae45d5-fca5-4709-bed9-3dd8efdba54e
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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