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dc.contributor.authorVogt, M
dc.contributor.authorLinton, O
dc.date.accessioned2019-02-13T00:31:13Z
dc.date.available2019-02-13T00:31:13Z
dc.date.issued2017-01-03
dc.identifier.issn1369-7412
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/289338
dc.description.abstractWe investigate a longitudinal data model with non-parametric regression functions that may vary across the observed individuals. In a variety of applications, it is natural to impose a group structure on the regression curves. Specifically, we may suppose that the observed individuals can be grouped into a number of classes whose members all share the same regression function. We develop a statistical procedure to estimate the unknown group structure from the data. Moreover, we derive the asymptotic properties of the procedure and investigate its finite sample performance by means of a simulation study and a real data example.
dc.publisherWiley
dc.titleClassification of non-parametric regression functions in longitudinal data models
dc.typeArticle
prism.endingPage27
prism.issueIdentifier1
prism.publicationDate2017
prism.publicationNameJournal of the Royal Statistical Society. Series B: Statistical Methodology
prism.startingPage5
prism.volume79
dc.identifier.doi10.17863/CAM.36587
dcterms.dateAccepted2016-02-17
rioxxterms.versionofrecord10.1111/rssb.12155
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2017-01-03
dc.contributor.orcidLinton, Oliver [0000-0003-2313-0564]
dc.identifier.eissn1467-9868
dc.publisher.urlhttps://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssb.12155
rioxxterms.typeJournal Article/Review
cam.issuedOnline2016-02-17
dc.identifier.urlhttps://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssb.12155
rioxxterms.freetoread.startdate2018-01-01


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