Data Fusion Using Empirical Likelihood

The authors propose a robust semi-parametric empirical likelihood method to integrate all available information from multiple samples with a common center of measurements. Two different sets of estimating equations are used to improve the classical likelihood inference on the measurement center. The proposed method does not require the knowledge of the functional forms of the probability density functions of related populations. The advantages of the proposed method are demonstrated through extensive simulation studies by comparing the mean squared errors, coverage probabilities and average lengths of confidence intervals with those from the classical likelihood method. Simulation results suggest that our approach provides more informative and efficient inference than the conventional maximum likelihood estimator if certain structural relationship exists among the parameters of relevant samples.