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Generalized Bayesian Record Linkage and Regression with Exact Error Propagation

  • Rebecca C. SteortsEmail author
  • Andrea Tancredi
  • Brunero Liseo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11126)

Abstract

Record linkage (de-duplication or entity resolution) is the process of merging noisy databases to remove duplicate entities. While record linkage removes duplicate entities from such databases, the downstream task is any inferential, predictive, or post-linkage task on the linked data. One goal of the downstream task is obtaining a larger reference data set, allowing one to perform more accurate statistical analyses. In addition, there is inherent record linkage uncertainty passed to the downstream task. Motivated by the above, we propose a generalized Bayesian record linkage method and consider multiple regression analysis as the downstream task. Records are linked via a random partition model, which allows for a wide class to be considered. In addition, we jointly model the record linkage and downstream task, which allows one to account for the record linkage uncertainty exactly. Moreover, one is able to generate a feedback propagation mechanism of the information from the proposed Bayesian record linkage model into the downstream task. This feedback effect is essential to eliminate potential biases that can jeopardize resulting downstream task. We apply our methodology to multiple linear regression, and illustrate empirically that the “feedback effect” is able to improve the performance of record linkage.

Notes

Acknowledgments

Steorts was supported by NSF-1652431 and NSF-1534412. Tancredi and Liseo were supported by Ministero dell’ Istruzione dell’ Universita e della Ricerca, Italia PRIN 2015.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Rebecca C. Steorts
    • 1
    Email author
  • Andrea Tancredi
    • 2
  • Brunero Liseo
    • 2
  1. 1.Department of Statistical Science, affiliated faculty, Computer Science, Biostatistics and Bioinformatics, the information initiative at Duke (iiD), and the Social Science Research Institute (SSRI) Duke University; Principal Researcher, Center for Statistical Research MethodologyDuke University and U.S. Census BureauDurhamUSA
  2. 2.Department of Methods and Models for Economics, Territory and FinanceLa SapienzaRomeItaly

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