Bayesian Estimation of Bipartite Matchings for Record Linkage
提出一种贝叶斯方法估计两个数据文件间的二分匹配，不假设匹配状态独立，能量化不确定性并优于传统方法，适用于合并不同来源的数据。
The bipartite record linkage task consists of merging two disparate datafiles containing information on two overlapping sets of entities. This is nontrivial in the absence of unique identifiers and it is important for a wide variety of applications given that it needs to be solved whenever we have to combine information from different sources. Most statistical techniques currently used for record linkage are derived from a seminal article by Fellegi and Sunter in 1969 Fellegi, I. P., and Sunter, A. B. (1969), “A Theory for Record Linkage,” Journal of the American Statistical Association, 64, 1183–1210.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]. These techniques usually assume independence in the matching statuses of record pairs to derive estimation procedures and optimal point estimators. We argue that this independence assumption is unreasonable and instead target a bipartite matching between the two datafiles as our parameter of interest. Bayesian implementations allow us to quantify uncertainty on the matching decisions and derive a variety of point estimators using different loss functions. We propose partial Bayes estimates that allow uncertain parts of the bipartite matching to be left unresolved. We evaluate our approach to record linkage using a variety of challenging scenarios and show that it outperforms the traditional methodology. We illustrate the advantages of our methods merging two datafiles on casualties from the civil war of El Salvador. Supplementary materials for this article are available online.