Quantile Regression With Measurement Error

Ying Wei, Raymond J. Carroll

Research output: Contribution to journalArticlepeer-review

71 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)1129-1143
Number of pages15
JournalJournal of the American Statistical Association
Volume104
Issue number487
DOIs
StatePublished - Aug 27 2009
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: 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.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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