Corrected Likelihood Estimation in Semiparametric Linear Mixed Measurement Error Models: Asymptotic Results

This paper is concerned with the estimation problem in semiparametric linear mixed models when some of the covariates are measured with errors. The authors proposed the corrected score function estimators for the parametric and non parametric components. The resulting estimators are shown to be consistent and asymptotically normal. An iterative algorithm is proposed for estimating the parameters. Asymptotic normality of the estimators is also derived. Finite sample performance of the proposed estimators is assessed by Monte Carlo simulation studies. We further illustrate the proposed procedures by an application.