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A weighting approach to making inference with probabilistically linked data

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  • James Chipperfield

Abstract

Record linkage is the act of bringing together records from two files that are believed to belong to the same unit (e.g., a person or business). It is a low‐cost way of increasing the set of variables available for analysis. Errors may arise in the linking process if an error‐free unit identifier is not available. Two types of linking errors include an incorrect link (records belonging to two different units are linked) and a missed record (an unlinked record for which a correct link exists). Naively ignoring linkage errors may mean that analysis of the linked file is biased. This paper outlines a “weighting approach” to making correct inference about regression coefficients and population totals in the presence of such linkage errors. This approach is designed for analysts who do not have the expertise or time to use specialist software required by other approaches but who are comfortable using weights in inference. The performance of the estimator is demonstrated in a simulation study.

Suggested Citation

  • James Chipperfield, 2019. "A weighting approach to making inference with probabilistically linked data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 73(3), pages 333-350, August.
    • Handle: RePEc:bla:stanee:v:73:y:2019:i:3:p:333-350
    DOI: 10.1111/stan.12172
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    Cited by:

    1. James Chipperfield, 2022. "Survey Weighting after Imperfect Linkage to an Administrative File," International Statistical Review, International Statistical Institute, vol. 90(3), pages 419-436, December.

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