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Article: Censored quantile regression with covariate measurement errors

TitleCensored quantile regression with covariate measurement errors
Authors
KeywordsAveraging estimation
Bootstrap
Errors-in-variables problem
Regression quantiles
Semiparametric method
Survival data
Issue Date2011
PublisherAcademia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/
Citation
Statistica Sinica, 2011, v. 21 n. 2, p. 949-971 How to Cite?
AbstractWe study censored quantile regression with covariates measured with errors. We propose a composite quantile objective function based on inverse censoringprobability weighting, and an averaging estimator to improve estimation efficiency. Our procedure can eliminate the bias in the naive estimator that is obtained by treating mismeasured covariates as error-free. Using a combination of martingale and quantile regression techniques, we show that the proposed estimators for the regression coefficients are consistent and asymptotically normal. We conducted simulation studies to examine the finite-sample properties of the new method, and demonstrated efficiency gain of the averaging estimator over the single quantile regression estimator. For illustration, we applied our model to a lung cancer study.
Persistent Identifierhttp://hdl.handle.net/10722/139721
ISSN
2015 Impact Factor: 0.838
2015 SCImago Journal Rankings: 2.292
References

 

DC FieldValueLanguage
dc.contributor.authorMa, Yen_HK
dc.contributor.authorYin, Gen_HK
dc.date.accessioned2011-09-23T05:54:47Z-
dc.date.available2011-09-23T05:54:47Z-
dc.date.issued2011en_HK
dc.identifier.citationStatistica Sinica, 2011, v. 21 n. 2, p. 949-971en_HK
dc.identifier.issn1017-0405en_HK
dc.identifier.urihttp://hdl.handle.net/10722/139721-
dc.description.abstractWe study censored quantile regression with covariates measured with errors. We propose a composite quantile objective function based on inverse censoringprobability weighting, and an averaging estimator to improve estimation efficiency. Our procedure can eliminate the bias in the naive estimator that is obtained by treating mismeasured covariates as error-free. Using a combination of martingale and quantile regression techniques, we show that the proposed estimators for the regression coefficients are consistent and asymptotically normal. We conducted simulation studies to examine the finite-sample properties of the new method, and demonstrated efficiency gain of the averaging estimator over the single quantile regression estimator. For illustration, we applied our model to a lung cancer study.en_HK
dc.languageengen_US
dc.publisherAcademia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/en_HK
dc.relation.ispartofStatistica Sinicaen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong Licenseen_US
dc.subjectAveraging estimationen_HK
dc.subjectBootstrapen_HK
dc.subjectErrors-in-variables problemen_HK
dc.subjectRegression quantilesen_HK
dc.subjectSemiparametric methoden_HK
dc.subjectSurvival dataen_HK
dc.titleCensored quantile regression with covariate measurement errorsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1017-0405&volume=21&issue=2&spage=949&epage=971&date=2011&atitle=Censored+quantile+regression+with+covariate+measurement+errors-
dc.identifier.emailYin, G: gyin@hku.hken_HK
dc.identifier.authorityYin, G=rp00831en_HK
dc.description.naturepublished_or_final_versionen_US
dc.identifier.scopuseid_2-s2.0-79952602599en_HK
dc.identifier.hkuros195647en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79952602599&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume21en_HK
dc.identifier.issue2en_HK
dc.identifier.spage949en_HK
dc.identifier.epage971en_HK
dc.publisher.placeTaiwan, Republic of Chinaen_HK
dc.identifier.scopusauthoridMa, Y=8908626500en_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK

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