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A Bayesian Approach to Graphical Record Linkage and Deduplication

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  • Rebecca C. Steorts
  • Rob Hall
  • Stephen E. Fienberg

Abstract

We propose an unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files. Our key innovation involves the representation of the pattern of links between records as a bipartite graph, in which records are directly linked to latent true individuals, and only indirectly linked to other records. This flexible representation of the linkage structure naturally allows us to estimate the attributes of the unique observable people in the population, calculate transitive linkage probabilities across records (and represent this visually), and propagate the uncertainty of record linkage into later analyses. Our method makes it particularly easy to integrate record linkage with post-processing procedures such as logistic regression, capture–recapture, etc. Our linkage structure lends itself to an efficient, linear-time, hybrid Markov chain Monte Carlo algorithm, which overcomes many obstacles encountered by previously record linkage approaches, despite the high-dimensional parameter space. We illustrate our method using longitudinal data from the National Long Term Care Survey and with data from the Italian Survey on Household and Wealth, where we assess the accuracy of our method and show it to be better in terms of error rates and empirical scalability than other approaches in the literature. Supplementary materials for this article are available online.

Suggested Citation

  • Rebecca C. Steorts & Rob Hall & Stephen E. Fienberg, 2016. "A Bayesian Approach to Graphical Record Linkage and Deduplication," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1660-1672, October.
  • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:516:p:1660-1672
    DOI: 10.1080/01621459.2015.1105807
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    Citations

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    Cited by:

    1. Daniel H. Weinberg & John M. Abowd & Robert F. Belli & Noel Cressie & David C. Folch & Scott H. Holan & Margaret C. Levenstein & Kristen M. Olson & Jerome P. Reiter & Matthew D. Shapiro & Jolene Smyth, 2017. "Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?," Working Papers 17-59r, Center for Economic Studies, U.S. Census Bureau.
    2. Betancourt, Brenda & Sosa, Juan & Rodríguez, Abel, 2022. "A prior for record linkage based on allelic partitions," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    3. John M. Abowd & Joelle Hillary Abramowitz & Margaret Catherine Levenstein & Kristin McCue & Dhiren Patki & Trivellore Raghunathan & Ann Michelle Rodgers & Matthew D. Shapiro & Nada Wasi & Dawn Zinsser, 2021. "Finding Needles in Haystacks: Multiple-Imputation Record Linkage Using Machine Learning," Working Papers 22-11, Federal Reserve Bank of Boston.
    4. Jana Asher & Dean Resnick & Jennifer Brite & Robert Brackbill & James Cone, 2020. "An Introduction to Probabilistic Record Linkage with a Focus on Linkage Processing for WTC Registries," IJERPH, MDPI, vol. 17(18), pages 1-16, September.
    5. Sabyasachi Bera & Snigdhansu Chatterjee, 2020. "High dimensional, robust, unsupervised record linkage," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 123-143, August.
    6. Duncan Smith, 2020. "Re‐identification in the Absence of Common Variables for Matching," International Statistical Review, International Statistical Institute, vol. 88(2), pages 354-379, August.
    7. Beręsewicz Maciej, 2019. "Correlates of Representation Errors in Internet Data Sources for Real Estate Market," Journal of Official Statistics, Sciendo, vol. 35(3), pages 509-529, September.
    8. Angelo Moretti & Natalie Shlomo, 2023. "Improving Probabilistic Record Linkage Using Statistical Prediction Models," International Statistical Review, International Statistical Institute, vol. 91(3), pages 368-394, December.
    9. Vo, Thanh Huan & Chauvet, Guillaume & Happe, André & Oger, Emmanuel & Paquelet, Stéphane & Garès, Valérie, 2023. "Extending the Fellegi-Sunter record linkage model for mixed-type data with application to the French national health data system," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    10. Bera Sabyasachi & Chatterjee Snigdhansu, 2020. "High dimensional, robust, unsupervised record linkage," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 123-143, August.

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