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The authors propose a robust semi-parametric empirical likelihood method tointegrate all available information from multiple samples with a common centerof measurements.  Two different sets of estimating equations are used to improve the classicallikelihood inference on the measurement center.  The proposed method does not require the knowledge of the functional forms ofthe probability density functions of related populations.  The advantages of the proposed method were demonstrated through the extensivesimulation studies by comparing mean squared error, coverage probabilities andaverage length of confidence intervals with those from the classical likelihoodmethod. Simulation results suggest that our approach provides more informativeand efficient inference than the conventional maximum likelihood estimator whencertain structural relationships exist among the parameters for these relevantsamples.

Data Fusion Using Robust Empirical Likelihood Inference