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Activity Number: 465 - Probabilistic Record Linkage: Better Assumptions, Scalable Inference, and Accounting for Uncertainty
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Social Statistics Section
Abstract #329092
Title: Bayesian Record Linkage with Sub-Models
Author(s): Joan Heck*
Keywords: Bayesian; Record Linkage; Voting

Record linkage is used to merge data sets in the absence of unique identifiers, relying on comparisons of shared fields to do so. Most common algorithms assume independence of these field comparisons conditional on whether or not they correspond to the same entity. We propose a Bayesian approach to record linkage with a loosened assumption of conditional independence, allowing the conditional distribution of comparisons to change based on the values of other fields. We apply our model to North Carolina 2016 voting data, linking records of provisional voters to the 2016 NC voter file.

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