ArticleJournal of applied statistics2024
A Bayesian approach for de-duplication in the presence of relational data.
Article in Journal of applied statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
In this paper, we study the impact of combining profile and network data in solving record de-duplication problems. We also assess the influence of a range of prior distributions on the linkage structure, and explore the use of stochastic gradient Hamiltonian Monte Carlo methods as a faster alternative to obtain samples from the posterior distribution for network parameters. Our methodology is evaluated using the RLdata500 data, which is a popular dataset in the record linkage literature.
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