A HIERARCHICAL BAYESIAN APPROACH TO RECORD LINKAGE AND POPULATION SIZE PROBLEMS
A HIERARCHICAL BAYESIAN APPROACH TO RECORD LINKAGE AND POPULATION SIZE PROBLEMS
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DOI：
10.1214/10-aoas447
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发表时间：
2011-06-01
影响因子：
1.8
通讯作者：
Liseo, Brunero
中科院分区：
文献类型：
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作者：
Tancredi, Andrea;Liseo, Brunero
我们提出并说明了一个分层贝叶斯方法匹配的统计记录在不同的场合观察。我们展示了如何在记录链接问题和捕获-再捕获设置中有效地采用这种模型，其中有限人口的大小是感兴趣的真实的对象。在拟议的基于模型的方法和目前的记录链接做法之间至少有两个重要的区别。首先，统计模型建立在实际观察到的分类变量上，并且不减少（0-1比较）可用信息。其次，模型的分层结构允许参数估计步骤和匹配过程之间的不确定性的双向传播，因此不使用插入式估计，并且在估计总体大小和执行记录链接时都考虑了正确的不确定性。我们通过一个真实的数据例子和模拟来说明和激励我们的建议。
We propose and illustrate a hierarchical Bayesian approach for matching statistical records observed on different occasions. We show how this model can be profitably adopted both in record linkage problems and in capture-recapture setups, where the size of a finite population is the real object of interest. There are at least two important differences between the proposed model-based approach and the current practice in record linkage. First, the statistical model is built up on the actually observed categorical variables and no reduction (to 0-1 comparisons) of the available information takes place. Second, the hierarchical structure of the model allows a two-way propagation of the uncertainty between the parameter estimation step and the matching procedure so that no plug-in estimates are used and the correct uncertainty is accounted for both in estimating the population size and in performing the record linkage. We illustrate and motivate our proposal through a real data example and simulations.