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Article: Power-transformed linear quantile regression with censored data

TitlePower-transformed linear quantile regression with censored data
Authors
KeywordsAsymptotic normality
Box-Cox transformation
Empirical estimation
Median regression
Random censoring
Survival data
Transformation 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. 1214-1224 How to Cite?
AbstractWe propose a class of power-transformed linear quantile regression models for survival data subject to random censoring. The estimation procedure follows two sequential steps. First, for a given transformation parameter, we can easily obtain the estimates for the regression coefficients by minimizing a well-defined convex objective function. Second, we can estimate the transformation parameter based on a model discrepancy measure by constructing cumulative sum processes. We show that both the regression and transformation parameter estimates are strongly consistent and asymptotically normal. The variance-covariance matrix depends on the unknown density function of the error term, so we estimate the variance by the usual bootstrap approach. We examine the performance of the proposed method for finite sample sizes through simulation studies and illustrate it with a real data example. © 2008 American Statistical Association.
Persistent Identifierhttp://hdl.handle.net/10722/146590
ISSN
2015 Impact Factor: 1.725
2015 SCImago Journal Rankings: 3.447
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYin, Gen_HK
dc.contributor.authorZeng, Den_HK
dc.contributor.authorLi, Hen_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. 1214-1224en_HK
dc.identifier.issn0162-1459en_HK
dc.identifier.urihttp://hdl.handle.net/10722/146590-
dc.description.abstractWe propose a class of power-transformed linear quantile regression models for survival data subject to random censoring. The estimation procedure follows two sequential steps. First, for a given transformation parameter, we can easily obtain the estimates for the regression coefficients by minimizing a well-defined convex objective function. Second, we can estimate the transformation parameter based on a model discrepancy measure by constructing cumulative sum processes. We show that both the regression and transformation parameter estimates are strongly consistent and asymptotically normal. The variance-covariance matrix depends on the unknown density function of the error term, so we estimate the variance by the usual bootstrap approach. We examine the performance of the proposed method for finite sample sizes through simulation studies and illustrate it with a real data example. © 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.subjectBox-Cox transformationen_HK
dc.subjectEmpirical estimationen_HK
dc.subjectMedian regressionen_HK
dc.subjectRandom censoringen_HK
dc.subjectSurvival dataen_HK
dc.subjectTransformation modelen_HK
dc.titlePower-transformed linear quantile regression with censored dataen_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.doi10.1198/016214508000000490en_HK
dc.identifier.scopuseid_2-s2.0-54949151496en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-54949151496&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume103en_HK
dc.identifier.issue483en_HK
dc.identifier.spage1214en_HK
dc.identifier.epage1224en_HK
dc.identifier.eissn1537-274X-
dc.identifier.isiWOS:000260193700031-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.scopusauthoridZeng, D=8725807700en_HK
dc.identifier.scopusauthoridLi, H=8423900800en_HK
dc.identifier.citeulike3389107-

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