Quantile regression estimation for distortion measurement error data

Jun Zhang, Jiefei Wang, Cuizhen Niu, Ming Sun

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)5107-5126
Number of pages20
JournalCommunications in Statistics - Theory and Methods
Issue number20
StatePublished - Oct 18 2018
Externally publishedYes


  • Composite quantile regression
  • Distortion measurement errors
  • Local linear smoothing
  • Quantile regression
  • Relative asymptotic efficiency

ASJC Scopus subject areas

  • Statistics and Probability


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