Data-Driven Model Checking for Errors-In-Variables Varying-Coefficient Models with Replicate Measurements

Miaomiao Wang, Catherine Chunling Liu, Tianfa Xie, Zhihua Sun (Corresponding Author)

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


In this work, the adequacy check of errors-in-variables varying-coefficient models is investigated when replicate measurements are available. Estimation using the naive method that ignores measurement errors is biased. After the calibration of the estimators of the regression coefficient functions, we construct an empirical-process-based test statistic by the attenuation of corrected residuals. The asymptotic properties of the test statistic under the null hypothesis, global and various local alternatives are established. Simulation studies and real data analyses reveal that the proposed test performs satisfactorily.
Original languageEnglish
Pages (from-to)12-27
Number of pages16
JournalComputational Statistics and Data Analysis
Publication statusPublished - Jan 2020


  • Additive measurement error
  • Empirical process
  • Model check
  • Replicate measurements
  • Varying-coefficient models

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


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