Statistical Analysis with Linked Data
Statistical Analysis with Linked Data
复制标题
DOI：
10.1111/insr.12295
复制
发表时间：
2018-10
影响因子：
2
通讯作者：
Ying Han;P. Lahiri
中科院分区：
文献类型：
--
作者：
Ying Han;P. Lahiri
当无法获得包含所有必要信息的单个数据集或收集额外变量的数据既耗时又成本高昂时，计算机化记录链接方法可帮助我们将来自不同来源的多个数据集联合收割机结合起来。链接错误在链接的数据集中是不可避免的，因为没有无错误的唯一标识符。少量的连锁误差会导致统计模型参数估计的实质性偏差和可变性增加。在本文中，我们提出了一个统一的理论与链接数据的统计分析。我们提出的方法，不像那些可用于二级数据分析的链接数据，利用记录链接过程数据作为一种替代，采取昂贵的样本，以评估错误率的记录链接过程。我们引入了一种刀切法来估计我们所提出的估计量的偏差、协方差阵和均方误差。仿真结果来评估所提出的估计，占联动误差的性能。
Computerised Record Linkage methods help us combine multiple data sets from different sources when a single data set with all necessary information is unavailable or when data collection on additional variables is time consuming and extremely costly. Linkage errors are inevitable in the linked data set because of the unavailability of error‐free unique identifiers. A small amount of linkage errors can lead to substantial bias and increased variability in estimating parameters of a statistical model. In this paper, we propose a unified theory for statistical analysis with linked data. Our proposed method, unlike the ones available for secondary data analysis of linked data, exploits record linkage process data as an alternative to taking a costly sample to evaluate error rates from the record linkage procedure. A jackknife method is introduced to estimate bias, covariance matrix and mean squared error of our proposed estimators. Simulation results are presented to evaluate the performance of the proposed estimators that account for linkage errors.