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Quantile regression estimation for distortion measurement error data

Author

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  • Jun Zhang
  • Jiefei Wang
  • Cuizhen Niu
  • Ming Sun

Abstract

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.

Suggested Citation

  • Jun Zhang & Jiefei Wang & Cuizhen Niu & Ming Sun, 2018. "Quantile regression estimation for distortion measurement error data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(20), pages 5107-5126, October.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:20:p:5107-5126
    DOI: 10.1080/03610926.2017.1386319
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    Cited by:

    1. Jun Zhang & Yiping Yang & Gaorong Li, 2020. "Logarithmic calibration for multiplicative distortion measurement errors regression models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(4), pages 462-488, November.

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