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Article: Sieve maximum likelihood estimator for semiparametric regression models with current status data

TitleSieve maximum likelihood estimator for semiparametric regression models with current status data
Authors
KeywordsAsymptotically efficient estimator
Maximum likelihood estimator
Optimal convergence rate
Partial linear model
Strongly consistent
Issue Date2004
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, 2004, v. 99 n. 466, p. 346-356 How to Cite?
AbstractIn a randomized controlled clinical trial study where the response variable of interest is the time to occurrence of a certain event, it is often too expensive or even impossible to observe the exact time. However, the current status of the subject at a random time of inspection is much more natural, feasible, and practical in terms of cost-effectiveness. This article considers a semiparametric regression model that consists of parametric and nonparametric regression components. A sieve maximum likelihood estimator (MLE) is proposed to estimate the regression parameter, allowing exploration of the nonlinear relationship between a certain covariate and the response function. Asymptotic properties of the proposed sieve MLEs are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. Moreover, the estimators of the unknown parameters are asymptotically efficient and normally distributed, and the estimator of the nonparametric function has an optimal convergence rate. Simulation studies were carried out to investigate the performance of the proposed method. For illustration purposes, the method is applied to a dataset from a study of the calcification of the hydrogel intraocular lenses, a complication of cataract treatment.
Persistent Identifierhttp://hdl.handle.net/10722/82662
ISSN
2015 Impact Factor: 1.725
2015 SCImago Journal Rankings: 3.447
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorXue, Hen_HK
dc.contributor.authorLam, KFen_HK
dc.contributor.authorLi, Gen_HK
dc.date.accessioned2010-09-06T08:31:58Z-
dc.date.available2010-09-06T08:31:58Z-
dc.date.issued2004en_HK
dc.identifier.citationJournal Of The American Statistical Association, 2004, v. 99 n. 466, p. 346-356en_HK
dc.identifier.issn0162-1459en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82662-
dc.description.abstractIn a randomized controlled clinical trial study where the response variable of interest is the time to occurrence of a certain event, it is often too expensive or even impossible to observe the exact time. However, the current status of the subject at a random time of inspection is much more natural, feasible, and practical in terms of cost-effectiveness. This article considers a semiparametric regression model that consists of parametric and nonparametric regression components. A sieve maximum likelihood estimator (MLE) is proposed to estimate the regression parameter, allowing exploration of the nonlinear relationship between a certain covariate and the response function. Asymptotic properties of the proposed sieve MLEs are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. Moreover, the estimators of the unknown parameters are asymptotically efficient and normally distributed, and the estimator of the nonparametric function has an optimal convergence rate. Simulation studies were carried out to investigate the performance of the proposed method. For illustration purposes, the method is applied to a dataset from a study of the calcification of the hydrogel intraocular lenses, a complication of cataract treatment.en_HK
dc.languageengen_HK
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.subjectAsymptotically efficient estimatoren_HK
dc.subjectMaximum likelihood estimatoren_HK
dc.subjectOptimal convergence rateen_HK
dc.subjectPartial linear modelen_HK
dc.subjectStrongly consistenten_HK
dc.titleSieve maximum likelihood estimator for semiparametric regression models with current status dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0162-1459&volume=99&issue=466&spage=346&epage=356&date=2004&atitle=Sieve+maximum+likelihood+estimator+for+semiparametric+regression+models+with+current+status+dataen_HK
dc.identifier.emailLam, KF: hrntlkf@hkucc.hku.hken_HK
dc.identifier.authorityLam, KF=rp00718en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1198/016214504000000313en_HK
dc.identifier.scopuseid_2-s2.0-2942599714en_HK
dc.identifier.hkuros88352en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-2942599714&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume99en_HK
dc.identifier.issue466en_HK
dc.identifier.spage346en_HK
dc.identifier.epage356en_HK
dc.identifier.isiWOS:000221572500005-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridXue, H=7202517221en_HK
dc.identifier.scopusauthoridLam, KF=8948421200en_HK
dc.identifier.scopusauthoridLi, G=22985864800en_HK

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