File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Generalized method of moments estimation for linear regression with clustered failure time data

TitleGeneralized method of moments estimation for linear regression with clustered failure time data
Authors
KeywordsAccelerated failure time model
Asymptotic normality
Correlated survival data
Estimation efficiency
Moment condition
Rank estimation
Semiparametric model
Issue Date2009
PublisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/
Citation
Biometrika, 2009, v. 96 n. 2, p. 293-306 How to Cite?
AbstractWe propose a generalized method of moments approach to the accelerated failure time model with correlated survival data. We study the semiparametric rank estimator using martingale-based moments. We circumvent direct estimation of correlation parameters by concatenating the moments and minimizing a quadratic objective function. We establish the consistency and asymptotic normality of the parameter estimators, and derive the limiting distribution of the objective function. We carry out simulation studies to examine the finite-sample properties of the method, and demonstrate its substantial efficiency gain over the conventional method. Finally, we illustrate the new proposal with an example from a diabetic retinopathy study. © 2009 Biometrika Trust.
Persistent Identifierhttp://hdl.handle.net/10722/139725
ISSN
2015 Impact Factor: 1.13
2015 SCImago Journal Rankings: 2.801
ISI Accession Number ID
References
Errata

 

DC FieldValueLanguage
dc.contributor.authorLi, Hen_HK
dc.contributor.authorYin, Gen_HK
dc.date.accessioned2011-09-23T05:54:48Z-
dc.date.available2011-09-23T05:54:48Z-
dc.date.issued2009en_HK
dc.identifier.citationBiometrika, 2009, v. 96 n. 2, p. 293-306en_HK
dc.identifier.issn0006-3444en_HK
dc.identifier.urihttp://hdl.handle.net/10722/139725-
dc.description.abstractWe propose a generalized method of moments approach to the accelerated failure time model with correlated survival data. We study the semiparametric rank estimator using martingale-based moments. We circumvent direct estimation of correlation parameters by concatenating the moments and minimizing a quadratic objective function. We establish the consistency and asymptotic normality of the parameter estimators, and derive the limiting distribution of the objective function. We carry out simulation studies to examine the finite-sample properties of the method, and demonstrate its substantial efficiency gain over the conventional method. Finally, we illustrate the new proposal with an example from a diabetic retinopathy study. © 2009 Biometrika Trust.en_HK
dc.languageengen_US
dc.publisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/en_HK
dc.relation.ispartofBiometrikaen_HK
dc.subjectAccelerated failure time modelen_HK
dc.subjectAsymptotic normalityen_HK
dc.subjectCorrelated survival dataen_HK
dc.subjectEstimation efficiencyen_HK
dc.subjectMoment conditionen_HK
dc.subjectRank estimationen_HK
dc.subjectSemiparametric modelen_HK
dc.titleGeneralized method of moments estimation for linear regression with clustered failure time dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-3444&volume=96&issue=2&spage=293&epage=306&date=2009&atitle=Generalized+method+of+moments+estimation+for+linear+regression+with+clustered+failure+time+data-
dc.identifier.emailYin, G: gyin@hku.hken_HK
dc.identifier.authorityYin, G=rp00831en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/biomet/asp005en_HK
dc.identifier.scopuseid_2-s2.0-66249100528en_HK
dc.identifier.hkuros195659en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-66249100528&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume96en_HK
dc.identifier.issue2en_HK
dc.identifier.spage293en_HK
dc.identifier.epage306en_HK
dc.identifier.eissn1464-3510-
dc.identifier.isiWOS:000266344300004-
dc.publisher.placeUnited Kingdomen_HK
dc.relation.erratumdoi:10.1093/biomet/asp061-
dc.identifier.scopusauthoridLi, H=8423900800en_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.citeulike4788648-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats