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Regression analysis under incomplete linkage

Author

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  • Kim, Gunky
  • Chambers, Raymond

Abstract

Most probability-based methods used to link records from two distinct data sets corresponding to the same target population do not lead to perfect linkage, i.e. there are linkage errors in the merged data. Further, the linkage is often incomplete, in the sense that many records in the two data sets remain unmatched at the completion of the linkage process. This paper introduces methods that correct for the biases due to linkage errors and incomplete linkage when carrying out regression analysis using linked data. In particular, it focuses on the case where one of the linked data sets is a sample from the target population and the other is a register, i.e. it covers the entire target population.

Suggested Citation

  • Kim, Gunky & Chambers, Raymond, 2012. "Regression analysis under incomplete linkage," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2756-2770.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:9:p:2756-2770
    DOI: 10.1016/j.csda.2012.02.026
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    References listed on IDEAS

    as
    1. Jixian Wang & Peter Donnan, 2002. "Adjusting for missing record linkage in outcome studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(6), pages 873-884.
    2. P. Lahiri & Michael D. Larsen, 2005. "Regression Analysis With Linked Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 222-230, March.
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    Citations

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    Cited by:

    1. Han Ying, 2020. "Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 30-34, August.
    2. Ying Han, 2020. "Discussion of "Small area estimation: its evolution in five decades", by Malay Ghosh," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 30-34, August.
    3. Tatiana Komarova & Denis Nekipelov & Evgeny Yakovlev, 2018. "Identification, data combination, and the risk of disclosure," Quantitative Economics, Econometric Society, vol. 9(1), pages 395-440, March.
    4. 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.
    5. Hendrik van Broekhuizen, 2016. "Graduate unemployment and Higher Education Institutions in South Africa," Working Papers 08/2016, Stellenbosch University, Department of Economics.
    6. 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.
    7. 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.
    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. Ben Powell & Paul A. Smith, 2020. "Computing expectations and marginal likelihoods for permutations," Computational Statistics, Springer, vol. 35(2), pages 871-891, June.
    10. N. Salvati & E. Fabrizi & M. G. Ranalli & R. L. Chambers, 2021. "Small area estimation with linked data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(1), pages 78-107, February.

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