标题： 基于稳健经验似然推断的数据融合 显示英文标题

标题： Data Fusion Using Robust Empirical Likelihood Inference

摘要： 作者提出了一种稳健的半参数经验似然方法，以整合来自多个具有共同测量中心样本的所有可用信息。使用两组不同的估计方程来改进经典似然推断关于测量中心的结果。所提出的方法不需要已知相关总体概率密度函数的功能形式。通过广泛的模拟研究展示了所提出方法的优势，这些研究通过比较均方误差、置信区间的覆盖率概率和平均长度与经典似然方法的结果。模拟结果显示，当这些相关样本的参数之间存在某些结构关系时，我们的方法比传统的最大似然估计提供了更丰富且有效的推断。 显示英文摘要

摘要： 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.