Handle URI:
http://hdl.handle.net/10754/624964
Title:
Quantile Regression With Measurement Error
Authors:
Wei, Ying; Carroll, Raymond J.
Abstract:
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.
Citation:
Wei Y, Carroll RJ (2009) Quantile Regression With Measurement Error. Journal of the American Statistical Association 104: 1129–1143. Available: http://dx.doi.org/10.1198/jasa.2009.tm08420.
Publisher:
Informa UK Limited
Journal:
Journal of the American Statistical Association
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
27-Aug-2009
DOI:
10.1198/jasa.2009.tm08420
Type:
Article
ISSN:
0162-1459; 1537-274X
Sponsors:
Wei’s research was supported by the National Science Foundation (DMS-096568) and a career award from NIEHS Center for Environmental Health in Northern Manhattan (ES009089). Carroll’s research was supported by a grant from the National Cancer Institute (CA57030) and by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors thank Dr. Mary Beth Terry for kindly providing the NCPP adult data.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorWei, Yingen
dc.contributor.authorCarroll, Raymond J.en
dc.date.accessioned2017-06-12T13:52:08Z-
dc.date.available2017-06-12T13:52:08Z-
dc.date.issued2009-08-27en
dc.identifier.citationWei Y, Carroll RJ (2009) Quantile Regression With Measurement Error. Journal of the American Statistical Association 104: 1129–1143. Available: http://dx.doi.org/10.1198/jasa.2009.tm08420.en
dc.identifier.issn0162-1459en
dc.identifier.issn1537-274Xen
dc.identifier.doi10.1198/jasa.2009.tm08420en
dc.identifier.urihttp://hdl.handle.net/10754/624964-
dc.description.abstractRegression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.en
dc.description.sponsorshipWei’s research was supported by the National Science Foundation (DMS-096568) and a career award from NIEHS Center for Environmental Health in Northern Manhattan (ES009089). Carroll’s research was supported by a grant from the National Cancer Institute (CA57030) and by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors thank Dr. Mary Beth Terry for kindly providing the NCPP adult data.en
dc.publisherInforma UK Limiteden
dc.subjectCorrection for attenuationen
dc.subjectGrowth curvesen
dc.subjectLongitudinal dataen
dc.subjectMeasurement erroren
dc.subjectQuantile regressionen
dc.subjectRegression calibrationen
dc.subjectRegression quantilesen
dc.titleQuantile Regression With Measurement Erroren
dc.typeArticleen
dc.identifier.journalJournal of the American Statistical Associationen
dc.contributor.institutionDepartment of Biostatistics, Columbia University, 722West 168th St., New York, NY 10032, United Statesen
dc.contributor.institutionDepartment of Statistics, Texas A and M University, Nutrition and Toxicology, TAMU 3143, College Station, TX 77843-3143, United Statesen
kaust.grant.numberKUS-CI-016-04en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.