  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 were demonstrated through the extensive
simulation studies by comparing mean squared error, coverage probabilities and
average length 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 when
certain structural relationships exist among the parameters for these relevant
samples.
