TY - JOUR
T1 - Quantile regression estimation for distortion measurement error data
AU - Zhang, Jun
AU - Wang, Jiefei
AU - Niu, Cuizhen
AU - Sun, Ming
N1 - Funding Information:
Jun Zhang’s research was supported by the National Natural Science Foundation of China (Grant No. 11401391). Cuizhen Niu’s research was supported by the Fundamental Research Funds for the Central Universities, China Postdoctoral Science Foundation (Grant No. 2016M600951), and National Natural Science Foundation of China (Grant No. 11701034).
Publisher Copyright:
© 2018, © 2018 Taylor & Francis Group, LLC.
PY - 2018/10/18
Y1 - 2018/10/18
N2 - We study the quantile estimation methods for the distortion measurement error data when variables are unobserved and distorted with additive errors by some unknown functions of an observable confounding variable. After calibrating the error-prone variables, we propose the quantile regression estimation procedure and composite quantile estimation procedure. Asymptotic properties of the proposed estimators are established, and we also investigate the asymptotic relative efficiency compared with the least-squares estimator. Simulation studies are conducted to evaluate the performance of the proposed methods, and a real dataset is analyzed as an illustration.
AB - We study the quantile estimation methods for the distortion measurement error data when variables are unobserved and distorted with additive errors by some unknown functions of an observable confounding variable. After calibrating the error-prone variables, we propose the quantile regression estimation procedure and composite quantile estimation procedure. Asymptotic properties of the proposed estimators are established, and we also investigate the asymptotic relative efficiency compared with the least-squares estimator. Simulation studies are conducted to evaluate the performance of the proposed methods, and a real dataset is analyzed as an illustration.
KW - Composite quantile regression
KW - Distortion measurement errors
KW - Local linear smoothing
KW - Quantile regression
KW - Relative asymptotic efficiency
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U2 - 10.1080/03610926.2017.1386319
DO - 10.1080/03610926.2017.1386319
M3 - Article
AN - SCOPUS:85041325841
SN - 0361-0926
VL - 47
SP - 5107
EP - 5126
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
IS - 20
ER -