Regression Modeling and File Matching Using Possibly Erroneous Matching Variables
Regression Modeling and File Matching Using Possibly Erroneous Matching Variables
复制标题
使用可能错误的匹配变量进行回归建模和文件匹配
DOI：
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发表时间：
2016
影响因子：
2.4
通讯作者：
Jerome P. Reiter
中科院分区：
文献类型：
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作者：
Nicole M. Dalzell;Jerome P. Reiter
许多分析需要连接两个数据库中的记录，其中包含重叠的个体集。在没有唯一标识符的情况下，链接过程通常涉及对两个文件共有的一组分类变量（如人口统计数据）进行匹配。但是，通常结果的匹配是不精确的：匹配变量的某些交叉分类不会生成跨文件的唯一链接。此外，用于匹配的变量可能受到报告错误的影响，这在分析中引入了额外的不确定性。我们提出了一种贝叶斯文件匹配方法，旨在同时估计回归模型和匹配记录，当用于匹配的分类变量受到错误的影响。该方法依赖于一个分层模型，该模型包括(1)给定指示链接的向量的涉及两个文件中的变量的兴趣回归，(2)给定用于匹配的变量的真值的链接向量模型，(3)给定用于匹配的变量的真值的用于匹配的变量的报告值模型，以及(4)用于匹配的变量的真值模型。我们描述了从模型的后验分布中抽样的算法。我们使用人工数据和北卡罗来纳州教育记录中的数据来说明这种方法。
ABSTRACT Many analyses require linking records from two databases comprising overlapping sets of individuals. In the absence of unique identifiers, the linkage procedure often involves matching on a set of categorical variables, such as demographics, common to both files. Typically, however, the resulting matches are inexact: some cross-classifications of the matching variables do not generate unique links across files. Further, the variables used for matching can be subject to reporting errors, which introduce additional uncertainty in analyses. We present a Bayesian file matching methodology designed to estimate regression models and match records simultaneously when categorical variables used for matching are subject to errors. The method relies on a hierarchical model that includes (1) the regression of interest involving variables from the two files given a vector indicating the links, (2) a model for the linking vector given the true values of the variables used for matching, (3) a model for reported values of the variables used for matching given their true values, and (4) a model for the true values of the variables used for matching. We describe algorithms for sampling from the posterior distribution of the model. We illustrate the methodology using artificial data and data from education records in the state of North Carolina.