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Article: Functional Censored Quantile Regression

TitleFunctional Censored Quantile Regression
Authors
KeywordsB-spline
Censored quantile regression
Functional regression
Generalized approximate cross-validation
Time-varying effect
Issue Date2019
PublisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main
Citation
Journal of the American Statistical Association, 2019, Epub, p. 1-24 How to Cite?
AbstractWe propose a functional censored quantile regression model to describe the time-varying relationship between time-to-event outcomes and corresponding functional covariates. The time-varying effect is modeled as an unspecified function that is approximated via B-splines. A generalized approximate cross-validation method is developed to select the number of knots by minimizing the expected loss. We establish asymptotic properties of the method and the knot selection procedure. Furthermore, we conduct extensive simulation studies to evaluate the finite sample performance of our method. Finally, we analyze the functional relationship between ambulatory blood pressure trajectories and clinical outcome in stroke patients. The results reinforce the importance of the morning blood pressure surge phenomenon, whose effect has caught attention but remains controversial in the medical literature. Supplementary materials for this article are available online.
Persistent Identifierhttp://hdl.handle.net/10722/278923
ISSN
2017 Impact Factor: 2.297
2015 SCImago Journal Rankings: 3.447
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJiang, F-
dc.contributor.authorCheng, Q-
dc.contributor.authorYin, G-
dc.contributor.authorShen, H-
dc.date.accessioned2019-10-21T02:16:24Z-
dc.date.available2019-10-21T02:16:24Z-
dc.date.issued2019-
dc.identifier.citationJournal of the American Statistical Association, 2019, Epub, p. 1-24-
dc.identifier.issn0162-1459-
dc.identifier.urihttp://hdl.handle.net/10722/278923-
dc.description.abstractWe propose a functional censored quantile regression model to describe the time-varying relationship between time-to-event outcomes and corresponding functional covariates. The time-varying effect is modeled as an unspecified function that is approximated via B-splines. A generalized approximate cross-validation method is developed to select the number of knots by minimizing the expected loss. We establish asymptotic properties of the method and the knot selection procedure. Furthermore, we conduct extensive simulation studies to evaluate the finite sample performance of our method. Finally, we analyze the functional relationship between ambulatory blood pressure trajectories and clinical outcome in stroke patients. The results reinforce the importance of the morning blood pressure surge phenomenon, whose effect has caught attention but remains controversial in the medical literature. Supplementary materials for this article are available online.-
dc.languageeng-
dc.publisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main-
dc.relation.ispartofJournal of the American Statistical Association-
dc.rightsAOM/Preprint Before Accepted: his article has been accepted for publication in [JOURNAL TITLE], published by Taylor & Francis. AOM/Preprint After Accepted: This is an [original manuscript / preprint] of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI]. Accepted Manuscript (AM) i.e. Postprint This is an Accepted Manuscript of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI].-
dc.subjectB-spline-
dc.subjectCensored quantile regression-
dc.subjectFunctional regression-
dc.subjectGeneralized approximate cross-validation-
dc.subjectTime-varying effect-
dc.titleFunctional Censored Quantile Regression-
dc.typeArticle-
dc.identifier.emailJiang, F: feijiang@hku.hk-
dc.identifier.emailYin, G: gyin@hku.hk-
dc.identifier.emailShen, H: haipeng@hku.hk-
dc.identifier.authorityJiang, F=rp02185-
dc.identifier.authorityYin, G=rp00831-
dc.identifier.authorityShen, H=rp02082-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/01621459.2019.1602047-
dc.identifier.hkuros308037-
dc.identifier.volumeForthcoming-
dc.identifier.spage1-
dc.identifier.epage24-
dc.identifier.isiWOS:000470485900001-
dc.publisher.placeUnited States-

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