TY - JOUR
T1 - Quantile Regression With Measurement Error
AU - Wei, Ying
AU - Carroll, Raymond J.
N1 - 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.
PY - 2009/8/27
Y1 - 2009/8/27
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/624964
UR - http://www.tandfonline.com/doi/abs/10.1198/jasa.2009.tm08420
UR - http://www.scopus.com/inward/record.url?scp=70349759867&partnerID=8YFLogxK
U2 - 10.1198/jasa.2009.tm08420
DO - 10.1198/jasa.2009.tm08420
M3 - Article
SN - 0162-1459
VL - 104
SP - 1129
EP - 1143
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 487
ER -