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Statistical Analysis with Linked Data

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  • Ying Han
  • Partha Lahiri

Abstract

Computerised Record Linkage methods help us combine multiple data sets from different sources when a single data set with all necessary information is unavailable or when data collection on additional variables is time consuming and extremely costly. Linkage errors are inevitable in the linked data set because of the unavailability of error‐free unique identifiers. A small amount of linkage errors can lead to substantial bias and increased variability in estimating parameters of a statistical model. In this paper, we propose a unified theory for statistical analysis with linked data. Our proposed method, unlike the ones available for secondary data analysis of linked data, exploits record linkage process data as an alternative to taking a costly sample to evaluate error rates from the record linkage procedure. A jackknife method is introduced to estimate bias, covariance matrix and mean squared error of our proposed estimators. Simulation results are presented to evaluate the performance of the proposed estimators that account for linkage errors.

Suggested Citation

  • Ying Han & Partha Lahiri, 2019. "Statistical Analysis with Linked Data," International Statistical Review, International Statistical Institute, vol. 87(S1), pages 139-157, May.
    • Handle: RePEc:bla:istatr:v:87:y:2019:i:s1:p:s139-s157
    DOI: 10.1111/insr.12295
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    Cited by:

    1. 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.
    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. 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.
    4. 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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