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Bayesian Estimation of Bipartite Matchings for Record Linkage

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  • Mauricio Sadinle

Abstract

The bipartite record linkage task consists of merging two disparate datafiles containing information on two overlapping sets of entities. This is nontrivial in the absence of unique identifiers and it is important for a wide variety of applications given that it needs to be solved whenever we have to combine information from different sources. Most statistical techniques currently used for record linkage are derived from a seminal article by Fellegi and Sunter in 1969. These techniques usually assume independence in the matching statuses of record pairs to derive estimation procedures and optimal point estimators. We argue that this independence assumption is unreasonable and instead target a bipartite matching between the two datafiles as our parameter of interest. Bayesian implementations allow us to quantify uncertainty on the matching decisions and derive a variety of point estimators using different loss functions. We propose partial Bayes estimates that allow uncertain parts of the bipartite matching to be left unresolved. We evaluate our approach to record linkage using a variety of challenging scenarios and show that it outperforms the traditional methodology. We illustrate the advantages of our methods merging two datafiles on casualties from the civil war of El Salvador. Supplementary materials for this article are available online.

Suggested Citation

  • Mauricio Sadinle, 2017. "Bayesian Estimation of Bipartite Matchings for Record Linkage," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 600-612, April.
  • Handle: RePEc:taf:jnlasa:v:112:y:2017:i:518:p:600-612
    DOI: 10.1080/01621459.2016.1148612
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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. Ahfock, Daniel & Pyne, Saumyadipta & McLachlan, Geoffrey J., 2022. "Statistical file-matching of non-Gaussian data: A game theoretic approach," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    3. Huiping Xu & Xiaochun Li & Zuoyi Zhang & Shaun Grannis, 2022. "Score test for assessing the conditional dependence in latent class models and its application to record linkage," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1663-1687, November.
    4. Betancourt, Brenda & Sosa, Juan & Rodríguez, Abel, 2022. "A prior for record linkage based on allelic partitions," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    5. Li‐Chun Zhang & Tiziana Tuoto, 2021. "Linkage‐data linear regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 522-547, April.
    6. Thomas Stringham, 2022. "Fast Bayesian Record Linkage With Record-Specific Disagreement Parameters," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1509-1522, October.
    7. 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.
    8. 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).
    9. 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.
    10. Xinmei Yang & Abhishek Arora & Shao-Yu Jheng & Melissa Dell, 2023. "Quantifying Character Similarity with Vision Transformers," Papers 2305.14672, arXiv.org.
    11. 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.

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