Data Fusion Using Empirical Likelihood

Hsiao-Hsuan Wang, Yuehua Wu, Yuejiao Fu, Xiaogang Wang

Department of Mathematics and Statistics, York University, Toronto, Canada.

DOI: 10.4236/ojs.2012.25070                

Abstract

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 knowle- dge 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 proba- bilities 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.

Keywords

Data Fusion; Empirical Likelihood; Robust Estimation; Multiple Samples

Conflicts of Interest

The authors declare no conflicts of interest.

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