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Article: Least squares estimation of varying-coefficient hazard regression with application to breast cancer dose-intensity data

TitleLeast squares estimation of varying-coefficient hazard regression with application to breast cancer dose-intensity data
Authors
KeywordsAsymptotic normality
Censoreddata
Estimating equation
Kernel function
Local polynomial
Nonparametric estimation
Varying coefficient
Issue Date2009
PublisherStatistical Society of Canada. The Journal's web site is located at http://www.mat.ulaval.ca/cjs
Citation
Canadian Journal Of Statistics, 2009, v. 37 n. 4, p. 659-674 How to Cite?
AbstractTo enhance modeling flexibility, the authors propose a nonparametric hazard regression model, for which the ordinary and weighted least squares estimation and inference procedures are studied. The proposed model does not assume any parametric specifications on the covariate effects, which is suitable for exploring the nonlinear interactions between covariates, time and some exposure variable. The authors propose the local ordinary and weighted least squares estimators for the varying-coefficient functions and establish the corresponding asymptotic normality properties. Simulation studies are conducted to empirically examine the finite-sample performance of the new methods, and a real data example from a recent breast cancer study is used as an illustration. © 2009 Statistical Society of Canada.
Persistent Identifierhttp://hdl.handle.net/10722/139728
ISSN
2015 Impact Factor: 0.413
2015 SCImago Journal Rankings: 0.737
References

 

DC FieldValueLanguage
dc.contributor.authorYin, Gen_HK
dc.contributor.authorHui, Len_HK
dc.date.accessioned2011-09-23T05:54:48Z-
dc.date.available2011-09-23T05:54:48Z-
dc.date.issued2009en_HK
dc.identifier.citationCanadian Journal Of Statistics, 2009, v. 37 n. 4, p. 659-674en_HK
dc.identifier.issn0319-5724en_HK
dc.identifier.urihttp://hdl.handle.net/10722/139728-
dc.description.abstractTo enhance modeling flexibility, the authors propose a nonparametric hazard regression model, for which the ordinary and weighted least squares estimation and inference procedures are studied. The proposed model does not assume any parametric specifications on the covariate effects, which is suitable for exploring the nonlinear interactions between covariates, time and some exposure variable. The authors propose the local ordinary and weighted least squares estimators for the varying-coefficient functions and establish the corresponding asymptotic normality properties. Simulation studies are conducted to empirically examine the finite-sample performance of the new methods, and a real data example from a recent breast cancer study is used as an illustration. © 2009 Statistical Society of Canada.en_HK
dc.languageengen_US
dc.publisherStatistical Society of Canada. The Journal's web site is located at http://www.mat.ulaval.ca/cjsen_HK
dc.relation.ispartofCanadian Journal of Statisticsen_HK
dc.subjectAsymptotic normalityen_HK
dc.subjectCensoreddataen_HK
dc.subjectEstimating equationen_HK
dc.subjectKernel functionen_HK
dc.subjectLocal polynomialen_HK
dc.subjectNonparametric estimationen_HK
dc.subjectVarying coefficienten_HK
dc.titleLeast squares estimation of varying-coefficient hazard regression with application to breast cancer dose-intensity dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0319-5724&volume=37&issue=4&spage=659&epage=674&date=2009&atitle=Least+squares+estimation+of+varying-coefficient+hazard+regression+with+application+to+breast+cancer+dose-intensity++data-
dc.identifier.emailYin, G: gyin@hku.hken_HK
dc.identifier.authorityYin, G=rp00831en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/cjs.10036en_HK
dc.identifier.scopuseid_2-s2.0-74049090502en_HK
dc.identifier.hkuros195698en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-74049090502&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume37en_HK
dc.identifier.issue4en_HK
dc.identifier.spage659en_HK
dc.identifier.epage674en_HK
dc.publisher.placeCanadaen_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.scopusauthoridHui, L=35339173200en_HK

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