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t-Type corrected-loss estimation for error-in-variable model

Author

Listed:
  • Jiao Jin
  • Liang Zhu
  • Xingwei Tong
  • Kirsten K. Ness

Abstract

In this article, we consider a linear model in which the covariates are measured with errors. We propose a t-type corrected-loss estimation of the covariate effect, when the measurement error follows the Laplace distribution. The proposed estimator is asymptotically normal. In practical studies, some outliers that diminish the robustness of the estimation occur. Simulation studies show that the estimators are resistant to vertical outliers and an application of 6-minute walk test is presented to show that the proposed method performs well.

Suggested Citation

  • Jiao Jin & Liang Zhu & Xingwei Tong & Kirsten K. Ness, 2017. "t-Type corrected-loss estimation for error-in-variable model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(2), pages 616-627, January.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:2:p:616-627
    DOI: 10.1080/03610926.2014.1002934
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