Seasonal warranty prediction based on recurrent event data

dc.contributor.author Shan, Qianqian
dc.contributor.author Hong, Yili
dc.contributor.author Meeker, William
dc.contributor.author Meeker, William
dc.contributor.department Statistics
dc.date 2021-03-13T05:43:43.000
dc.date.accessioned 2021-04-30T12:17:54Z
dc.date.available 2021-04-30T12:17:54Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2020
dc.date.issued 2020-06-01
dc.description.abstract <p>Warranty return data from repairable systems, such as home appliances, lawn mowers, computers and automobiles, result in recurrent event data. The nonhomogeneous Poisson process (NHPP) model is used widely to describe such data. Seasonality in the repair frequencies and other variabilities, however, complicate the modeling of recurrent event data. Not much work has been done to address the seasonality, and this paper provides a general approach for the application of NHPP models with dynamic covariates to predict seasonal warranty returns. The methods presented here, however, can be applied to other applications that result in seasonal recurrent event data. A hierarchical clustering method is used to stratify the population into groups that are more homogeneous than the overall population. The stratification facilitates modeling the recurrent event data with both time-varying and time-constant covariates. We demonstrate and validate the models using warranty claims data for two different types of products. The results show that our approach provides important improvements in the predictive power of monthly events compared with models that do not take the seasonality and covariates into account.</p>
dc.description.comments <p>This article is published as Shan, Qianqian, Yili Hong, and William Q. Meeker. "Seasonal warranty prediction based on recurrent event data." <em>Annals of Applied Statistics</em> 14, no. 2 (2020): 929-955. doi:<a href="http://dx.doi.org/10.1214/20-AOAS1333" target="_blank">10.1214/20-AOAS1333</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/318/
dc.identifier.articleid 1320
dc.identifier.contextkey 22038011
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/318
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/105173
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/318/0-Institute_of_Mathematical_Statistics_copyrightTA.pdf|||Fri Jan 14 23:32:48 UTC 2022
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/318/2020_Meeker_SeasonalWarranty.pdf|||Fri Jan 14 23:32:49 UTC 2022
dc.source.uri 10.1214/20-AOAS1333
dc.subject.disciplines Applied Statistics
dc.subject.disciplines Probability
dc.subject.disciplines Statistical Models
dc.title Seasonal warranty prediction based on recurrent event 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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