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Article: Partially linear additive hazards regression with varying coefficients

TitlePartially linear additive hazards regression with varying coefficients
Authors
KeywordsAsymptotic normality
Censored data
Estimating equation
Kernel function
Local polynomial
Semiparametric estimation
Varying-coefficient model
Issue Date2008
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, 2008, v. 103 n. 483, p. 1200-1213 How to Cite?
AbstractTo explore the nonlinear interactions between some covariates and an exposure variable, we propose the partially linear additive hazards model for survival data. In a semiparametric setting, we construct a local pseudoscore function to estimate the varying and constant coefficients and establish the asymptotic normality of the proposed estimators. Moreover, we develop the weak convergence property for the local estimator of the baseline cumulative hazard function. We conduct simulation studies to empirically examine the finite-sample performance of the proposed methods and use real data from a breast cancer study for illustration. © 2008 American Statistical Association.
Persistent Identifierhttp://hdl.handle.net/10722/146589
ISSN
2015 Impact Factor: 1.725
2015 SCImago Journal Rankings: 3.447
ISI Accession Number ID
Funding AgencyGrant Number
Physician Referral Service at M. D. Anderson Cancer Center
U.S. Department of DefenseW81XWH-05-2-0027
Funding Information:

This work was supported in part by funds from the Physician Referral Service at M. D. Anderson Cancer Center and the U.S. Department of Defense grant W81XWH-05-2-0027. This work was conducted when Hui Li was visiting M. D. Anderson Cancer Center.

References

 

DC FieldValueLanguage
dc.contributor.authorYin, Gen_HK
dc.contributor.authorLi, Hen_HK
dc.contributor.authorZeng, Den_HK
dc.date.accessioned2012-05-02T08:37:14Z-
dc.date.available2012-05-02T08:37:14Z-
dc.date.issued2008en_HK
dc.identifier.citationJournal Of The American Statistical Association, 2008, v. 103 n. 483, p. 1200-1213en_HK
dc.identifier.issn0162-1459en_HK
dc.identifier.urihttp://hdl.handle.net/10722/146589-
dc.description.abstractTo explore the nonlinear interactions between some covariates and an exposure variable, we propose the partially linear additive hazards model for survival data. In a semiparametric setting, we construct a local pseudoscore function to estimate the varying and constant coefficients and establish the asymptotic normality of the proposed estimators. Moreover, we develop the weak convergence property for the local estimator of the baseline cumulative hazard function. We conduct simulation studies to empirically examine the finite-sample performance of the proposed methods and use real data from a breast cancer study for illustration. © 2008 American Statistical Association.en_HK
dc.languageengen_US
dc.publisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=mainen_HK
dc.relation.ispartofJournal of the American Statistical Associationen_HK
dc.subjectAsymptotic normalityen_HK
dc.subjectCensored dataen_HK
dc.subjectEstimating equationen_HK
dc.subjectKernel functionen_HK
dc.subjectLocal polynomialen_HK
dc.subjectSemiparametric estimationen_HK
dc.subjectVarying-coefficient modelen_HK
dc.titlePartially linear additive hazards regression with varying coefficientsen_HK
dc.typeArticleen_HK
dc.identifier.emailYin, G: gyin@hku.hken_HK
dc.identifier.authorityYin, G=rp00831en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1198/016214508000000463en_HK
dc.identifier.scopuseid_2-s2.0-54949146866en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-54949146866&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume103en_HK
dc.identifier.issue483en_HK
dc.identifier.spage1200en_HK
dc.identifier.epage1213en_HK
dc.identifier.eissn1537-274X-
dc.identifier.isiWOS:000260193700030-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.scopusauthoridLi, H=8423900800en_HK
dc.identifier.scopusauthoridZeng, D=8725807700en_HK
dc.identifier.citeulike3389108-

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