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Linear regression models with multiplicative distortions under new identifiability conditions

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  • Jun Zhang
  • Bingqing Lin
  • Yan Zhou

Abstract

This paper considers linear regression models when neither the response variable nor the covariates can be directly observed, but are measured with multiplicative distortion measurement errors. We propose new identifiability conditions for the distortion functions via the varying coefficient models, then moment‐based estimators of parameters in the model are proposed by using the estimated varying coefficient functions. This method does not require the independence condition between the confounding variables and the unobserved response and variables. We establish the connections among the varying coefficient based estimators, the conditional mean calibration and the conditional absolute mean calibration. We study the asymptotic results of these proposed estimators, and discuss their asymptotic efficiencies. Lastly, we make some comparisons among the proposed estimators through the simulation. These methods are applied to analyze a real dataset for an illustration.

Suggested Citation

  • Jun Zhang & Bingqing Lin & Yan Zhou, 2024. "Linear regression models with multiplicative distortions under new identifiability conditions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 78(1), pages 25-67, February.
    • Handle: RePEc:bla:stanee:v:78:y:2024:i:1:p:25-67
    DOI: 10.1111/stan.12304
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    References listed on IDEAS

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