Empirical Likelihood Using External Summary Information
Empirical Likelihood Using External Summary Information
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DOI：
10.5705/ss.202023.0056
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发表时间：
2023
影响因子：
1.4
通讯作者：
Lyu Ni;Junchao Shao;Jinyi Wang;Lei Wang
中科院分区：
文献类型：
--
作者：
Lyu Ni;Junchao Shao;Jinyi Wang;Lei Wang
现代科学研究中的统计分析现在有机会利用类似研究的外部摘要信息来获得效率。然而，为当前研究生成数据的群体（称为内部群体）通常不同于用于汇总信息的外部群体，尽管它们具有一些共同特征，使得效率提高成为可能。现有的人群异质性是一个具有挑战性的问题，特别是当我们只有汇总统计数据，但没有个人水平的外部数据。在本文中，我们应用经验似然方法来估计内部人口分布，外部汇总信息作为约束条件下的人口异质性的效率增益。我们证明了，在不使用任何外部信息的情况下，我们的方法产生了一个渐近更有效的估计内部人口分布与习惯的经验似然相比，在外部信息是基于一个数据集的大小比，
: Statistical analysis in modern scientific research nowadays has opportunities to utilize external summary information from similar studies to gain efficiency. However, the population generating data for current study, referred to as internal population, is typically different from the external population for summary information, although they share some common characteristics that make efficiency improvement possible. The existing population heterogeneity is a challenging issue especially when we have only summary statistics but not individual-level external data. In this paper, we apply an empirical likelihood approach to estimating internal population distribution, with external summary information utilized as constraints for efficiency gain under population heterogeneity. We show that our approach produces an asymptotically more efficient estimator of internal population distribution compared with the customary empirical likelihood without using any external information, under the condition that the external information is based on a dataset with size larger than that