A Method for Calibrating False-Match Rates in Record Linkage
提出一种基于混合模型的校准方法，通过EM算法估计记录链接中不同权重阈值对应的错误匹配率，并在人口普查数据中验证其有效性。
Abstract Specifying a record-linkage procedure requires both (1) a method for measuring closeness of agreement between records, typically a scalar weight, and (2) a rule for deciding when to classify records as matches or nonmatches based on the weights. Here we outline a general strategy for the second problem, that is, for accurately estimating false-match rates for each possible cutoff weight. The strategy uses a model where the distribution of observed weights are viewed as a mixture of weights for true matches and weights for false matches. An EM algorithm for fitting mixtures of transformed-normal distributions is used to find posterior modes; associated posterior variability is due to uncertainty about specific normalizing transformations as well as uncertainty in the parameters of the mixture model, the latter being calculated using the SEM algorithm. This mixture-model calibration method is shown to perform well in an applied setting with census data. Further, a simulation experiment reveals that, across a wide variety of settings not satisfying the model's assumptions, the procedure is slightly conservative on average in the sense of overstating false-match rates, and the onesided confidence coverage (i.e., the proportion of times that these interval estimates cover or overstate the actual false-match rate) is very close to the nominal rate.