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Correcting for linkage errors in contingency tables: A cautionary tale
Scholtus,Sander Shlomo,Natalie de Waal,Ton
Scholtus,Sander
Shlomo,Natalie
de Waal,Ton
Abstract
Record linkage aims to bring records together from two or more files that belong to the same statistical entity. Naïvely treating a linked file as if there are no linkage errors may lead to biased inference. We present two general approaches for compensating for linkage error when calculating and analysing a two-way contingency table for categorical data, and study the following question: under what conditions can a compensation approach improve on the naïve approach, where linkage error is not compensated for? To this end, we compare estimation errors, bias, variance and mean square error for the naïve approach and two compensation approaches by means of an analytical study as well as a simulation study.
Description
The second author was supported by the EPSRC grant EP/K032208/1 at the Isaac Newton Institute for Mathematical Sciences, Data Linkage and Anonymization Programme. The views expressed in this article are those of the authors and do not necessarily reflect the policies of Statistics Netherlands.
Date
2022
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Volume Title
Publisher
Research Projects
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Keywords
Contingency table, Exchangeable linkage error model, Linkage error correction, Probabilistic record linkage
Citation
Scholtus, S, Shlomo, N & de Waal, T 2022, 'Correcting for linkage errors in contingency tables : A cautionary tale', Journal of Statistical Planning and Inference, vol. 218, pp. 122-137. https://doi.org/10.1016/j.jspi.2021.10.004
License
info:eu-repo/semantics/openAccess