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Estimation of parameters in linear mixed measurement error models with stochastic linear restrictions

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  • Bahareh Yavarizadeh
  • Abdolrahman Rasekh
  • Babak Babadi

Abstract

In this paper, we concentrate on the estimation of parameters in the linear mixed models (LMMs) with stochastic linear restrictions on the fixed and random effects, when the fixed effects are measured with error. In addition, the asymptotic properties of the estimators are derived and their performances are compared with the estimators for no restrictions cases, in the sense of mean square error matrix (MSEM). Finally, a simulation study and numerical example have been conducted to show the superiority of the restricted estimator over the unrestricted estimator.

Suggested Citation

  • Bahareh Yavarizadeh & Abdolrahman Rasekh & Babak Babadi, 2020. "Estimation of parameters in linear mixed measurement error models with stochastic linear restrictions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(23), pages 5853-5865, December.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:23:p:5853-5865
    DOI: 10.1080/03610926.2019.1622730
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