Practical Bayesian Inference for Record Linkage

• Activity Number: 235 - Best Student Papers Awarded by the ASA Consortium of GSS/SSS/SRMS
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
• Sponsor: Survey Research Methods Section
• Author(s): Brendan McVeigh* and Jared S Murray
• Companies: Carnegie Mellon University and University of Texas at Austin
• Keywords: Record Linkage; Markov chain Monte Carlo; Mixture Models; Bayesian Inference

Abstract

Probabilistic record linkage (PRL) is the process of determining which records in two databases correspond to the same underlying entity in the absence of a unique identifier. Bayesian solutions to this problem provide a powerful mechanism for propagating uncertainty due to uncertain links between records (via the posterior distribution). However, computational considerations severely limit the practical applicability of existing Bayesian approaches. We propose a new computational approach, providing both a fast algorithm for deriving point estimates of the linkage structure that properly account for one-to-one matching and a restricted MCMC algorithm that samples from an approximate posterior distribution. Our advances make it possible to perform Bayesian PRL for larger problems, and to assess the sensitivity of results to varying prior specifications. We demonstrate the methods on simulated data and an application to a post-enumeration survey for coverage estimation in the Italian census.