A Comparison of Minimum Distance and Maximum Likelihood Estimation of a Mixture Proportion
通过模拟比较了混合正态模型中最小距离和最大似然估计的表现，发现正态假设下ML更优，但偏离正态时MD更稳健，且一种迭代初值方法偶尔优于两者。
Abstract The estimation of mixing proportions in the mixture model is discussed, with emphasis on the mixture of two normal components with all five parameters unknown. Simulations are presented that compare minimum distance (MD) and maximum likelihood (ML) estimation of the parameters of this mixture-of-normals model. Some practical issues of implementation of these results are also discussed. Simulation results indicate that ML techniques are superior to MD when component distributions actually are normal, but MD techniques provide better estimates than ML under symmetric departures from component normality. Interestingly, an ad hoc starting value for the iterative procedures occasionally outperformed both the ML and MD techniques. Results are presented that establish strong consistency and asymptotic normality of the MD estimator under conditions that include the mixture-of-normals model. Asymptotic variances and relative efficiencies are obtained for further comparison of the MD and ML estimators. Key Words: RobustnessRelative efficiencySimulationIterative routinesEM algorithm