The proposed estimators report robust estimates of linkage precision using simulations, indicating improved accuracy in data matching processes involving skewed distributions.
Deterministic record linkage is widely used when unique identifiers are unavailable. Multi-pass deterministic linkage increases the link rate, but evaluating the precision of each pass — that is, the proportion of correct links—is crucial for guiding linkage design. This paper proposes two methods for estimating pass-level precision. The first is an analytic estimator assuming uniformly distributed linking variables; the second is a replication-based estimator that avoids strong distributional assumptions. We demonstrate both methods through simulations using D-MAC, a deterministic linkage macro developed at the Australian Bureau of Statistics. Results show that while the analytic estimator performs well under uniformity, the replication approach provides robust estimates across a range of diverse scenarios, including those where linking variables have skewed distributions.
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Ma et al. (2025) studied this question.
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