Model averaging estimator for semiparametric mixed effects models with measurement errors

Model uncertainty always exists when modeling semiparametric mixed effects models with measurement errors. We propose a model averaging estimator to cope with model uncertainty by combining estimates from various candidate models with specific weights. The weight vector is obtained by minimizing a leave-subject-out cross-validation criterion. When all candidate models are misspecified, we demonstrate that our proposed method is asymptotically optimal as it achieves the lowest squared loss. Our method can assign all weights to the correctly specified models when the candidate model includes at least one correct model. Simulation studies and an empirical illustration demonstrate the potential of the proposed method compared to other methods.