Further Readings
Dusetzina, S. B., Tyree, S., Meyer, A. M., et al. (2014). Linking data for health services research: A framework and instructional guide. Rockville: Agency for Healthcare Research and Quality (US).
Fellegi, I. P., & Sunter, A. B. (1969). A theory for record linkage. Journal of the American Statistical Association, 64, 1183–1210.
Schumacher, S. (2007). Probabilistic versus deterministic data matching: Making an accurate decision, information management special reports. Washington, DC: The Office of the National Coordinator for Health Information Technology (ONC).
Winkler, W. E. (1999). The state of record linkage and current research problems. Washington, DC: Statistical Research Division, US Census Bureau.
Zhang, T., & Stevens, D. W. (2012). Integrated data system person identification: Accuracy requirements and methods. https://ssrn.com/abstract=2512590; https://doi.org/10.2139/ssrn.2512590.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this entry
Cite this entry
Zhang, T. (2018). Probabilistic Matching. In: Schintler, L., McNeely, C. (eds) Encyclopedia of Big Data. Springer, Cham. https://doi.org/10.1007/978-3-319-32001-4_501-1
Download citation
DOI: https://doi.org/10.1007/978-3-319-32001-4_501-1
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32001-4
Online ISBN: 978-3-319-32001-4
eBook Packages: Springer Reference Business and ManagementReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences