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.
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