A scaling approach to record linkage
Journal article
Goldstein, Harvey, Harron, Katie and Cortina-Borja, Mario. (2017). A scaling approach to record linkage. Statistics in Medicine. 36(16), pp. 2514 - 2521. https://doi.org/10.1002/sim.7287
Authors | Goldstein, Harvey, Harron, Katie and Cortina-Borja, Mario |
---|---|
Abstract | With increasing availability of large datasets 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. |
Year | 2017 |
Journal | Statistics in Medicine |
Journal citation | 36 (16), pp. 2514 - 2521 |
Publisher | John Wiley & Sons Ltd |
ISSN | 0277-6715 |
Digital Object Identifier (DOI) | https://doi.org/10.1002/sim.7287 |
Scopus EID | 2-s2.0-85017338633 |
Page range | 2514 - 2521 |
Research Group | Institute for Learning Sciences and Teacher Education (ILSTE) |
Publisher's version | File Access Level Controlled |
Place of publication | United Kingdom |
Editors | R. D'Agostino, S. Day and E. Goetghebeur and J. Greenhouse |
https://acuresearchbank.acu.edu.au/item/880q2/a-scaling-approach-to-record-linkage
Restricted files
Publisher's version
105
total views0
total downloads0
views this month0
downloads this month