Empirical likelihood inference for a common mean in the presence of heteroscedasticity

The authors develop empirical likelihood (EL) based methods of inference for a common mean using data from several independent but nonhomogeneous populations. For point estimation, they propose a maximum empirical likelihood (MEL) estimator and show that it is p n -consistent and asymptotically optimal. For confidence intervals, they consider two EL based methods an d show that both intervals have approximately correct coverage probabilities under large samples. Finite-sample performances of the MEL estimator and the EL based confidence intervals are evaluated through a s imulation study. The results indi- cate that overall the MEL estimator and the weighted EL confidence interval are superior alternatives to the existing methods.

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