Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data

dc.contributor.author Hong, Yili
dc.contributor.author Duan, Yuanyuan
dc.contributor.author Meeker, William
dc.contributor.author Meeker, William
dc.contributor.author Stanley, Deborah
dc.contributor.author Gu, Xiaohong
dc.contributor.department Statistics
dc.date 2019-09-22T17:24:47.000
dc.date.accessioned 2020-07-02T06:57:35Z
dc.date.available 2020-07-02T06:57:35Z
dc.date.issued 2015-05-01
dc.description.abstract <p>Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online.</p>
dc.description.comments <p>This is an article published by Taylor & Francis as Hong, Yili, Yuanyuan Duan, William Q. Meeker, Deborah L. Stanley, and Xiaohong Gu. "Statistical methods for degradation data with dynamic covariates information and an application to outdoor weathering data." <em>Technometrics</em> 57, no. 2 (2015): 180-193. DOI: <a href="http://dx.doi.org/10.1080/00401706.2014.915891" target="_blank">10.1080/00401706.2014.915891</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/272/
dc.identifier.articleid 1283
dc.identifier.contextkey 15238454
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/272
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90590
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/272/2015_MeekerWilliam_StatisticalMethodsDegradation.pdf|||Fri Jan 14 23:06:45 UTC 2022
dc.source.uri https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1082&context=stat_las_preprints
dc.subject.disciplines Statistics and Probability
dc.subject.keywords Covariate process
dc.subject.keywords Environmental conditions
dc.subject.keywords Lifetime prediction
dc.subject.keywords Organic coatings
dc.subject.keywords System health monitoring
dc.subject.keywords Usage history
dc.title Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering 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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