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A scaling approach to record linkage

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)2514-2521
Number of pages6
JournalStatistics in Medicine
Volume36
Issue number16
Early online date16 Mar 2017
DOIs
DateAccepted/In press - 2 Mar 2017
DateE-pub ahead of print - 16 Mar 2017
DatePublished (current) - 20 Jul 2017

Abstract

With increasing availability of large data sets derived from administrative and other sources, there is an increasing demand for the successful linking of these to provide rich sources of data for further analysis. Variation in the quality of identifiers used to carry out linkage means that existing approaches are often based upon ‘probabilistic’ models, which are based on a number of assumptions, and can make heavy computational demands. In this paper we suggest a new approach to classifying record pairs in linkage, based upon weights (scores) derived using a scaling algorithm. The proposed method does not rely on training data, is computationally fast, requires only moderate amounts of storage and has intuitive appeal.

    Research areas

  • scaling, record linkage, correspondence analysis, data linkage

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Wiley at http://onlinelibrary.wiley.com/doi/10.1002/sim.7287/abstract. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 568 KB, PDF-document

    Licence: CC BY-NC

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