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Regression Analysis under Probabilistic Multi‐Linkage

Author

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  • Gunky Kim
  • Raymond Chambers

Abstract

Linkage errors can occur when probability‐based methods are used to link records from two distinct data sets corresponding to the same target population. Current approaches to modifying standard methods of regression analysis to allow for these errors only deal with the case of two linked data sets and assume that the linkage process is complete, that is, all records on the two data sets are linked. This study extends these ideas to accommodate the situation when more than two data sets are probabilistically linked and the linkage is incomplete.

Suggested Citation

  • Gunky Kim & Raymond Chambers, 2012. "Regression Analysis under Probabilistic Multi‐Linkage," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(1), pages 64-79, February.
    • Handle: RePEc:bla:stanee:v:66:y:2012:i:1:p:64-79
    DOI: 10.1111/j.1467-9574.2011.00509.x
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    Cited by:

    1. Ray Chambers & Andrea Diniz da Silva, 2020. "Improved secondary analysis of linked data: a framework and an illustration," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 37-59, January.
    2. Catherine G. Massey, 2016. "Playing with Matches: An Assessment of Accuracy in Linked Historical Data," CARRA Working Papers 2016-05, Center for Economic Studies, U.S. Census Bureau.
    3. 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.
    4. Martha J. Bailey & Connor Cole & Morgan Henderson & Catherine Massey, 2020. "How Well Do Automated Linking Methods Perform? Lessons from US Historical Data," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 997-1044, December.
    5. 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.

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