<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Record linkage is the act of bringing together records that are believed to belong to the same unit (e.g., person or business) from two or more files. Record linkage is not an error-free process and can lead to linking a pair of records that do not belong to the same unit. This occurs because linking fields on the files, which ideally would uniquely identify each unit, are often imperfect. There has been an explosion of record linkage applications, particularly involving government agencies and in the field of health, yet there has been little work on making correct inference using such linked files. Naively treating a linked file as if it were linked without errors can lead to biased inferences. This article develops a method of making inferences for cross tabulated variables when record linkage is not an error-free process. In particular, it develops a parametric bootstrap approach to estimation which can accommodate the sophisticated probabilistic record linkage techniques that are widely used in practice (e.g., 1-1 linkage). The article demonstrates the effectiveness of this method in a simulation and in a real application.</p></abstract>

Record linkage is the act of bringing together records that are believed to belong to the same unit (e.g., person or business) from two or more files. Record linkage is not an error-free process and can lead to linking a pair of records that do not belong to the same unit. This occurs because linking fields on the files, which ideally would uniquely identify each unit, are often imperfect. There has been an explosion of record linkage applications, particularly involving government agencies and in the field of health, yet there has been little work on making correct inference using such linked files. Naively treating a linked file as if it were linked without errors can lead to biased inferences. This article develops a method of making inferences for cross tabulated variables when record linkage is not an error-free process. In particular, it develops a parametric bootstrap approach to estimation which can accommodate the sophisticated probabilistic record linkage techniques that are widely used in practice (e.g., 1-1 linkage). The article demonstrates the effectiveness of this method in a simulation and in a real application.

Using the Bootstrap to Account for Linkage Errors when Analysing Probabilistically Linked Categorical Data

Australian Bureau of Statistics, Methodology Division, P O Box 10, Belconnen, Australian Capital Territory 2616 Australia

University of Wollongong, National Institute for Applied Statistics Research, Northfields Avenue Wollongong, New South Wales, 2500 Australia

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Record linkage is the act of bringing together records that are believed to belong to the same unit (e.g., person or business) from two or more files. Record...

Using the Bootstrap to Account for Linkage Errors when Analysing Probabilistically Linked Categorical Data

Publié en ligne: 01 sept. 2015

Pages: 397 - 414

Reçu: 01 janv. 2014

Accepté: 01 déc. 2014

DOI: https://doi.org/10.1515/jos-2015-0024

Mots clés
Record linkage, measurement error, parametric bootstrap.

© by James O. Chipperfield

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Using the Bootstrap to Account for Linkage Errors when Analysing Probabilistically Linked Categorical Data

Publié en ligne: 01 sept. 2015

Pages: 397 - 414

Reçu: 01 janv. 2014

Accepté: 01 déc. 2014

DOI: https://doi.org/10.1515/jos-2015-0024

Mots clésRecord linkage, measurement error, parametric bootstrap.

© by James O. Chipperfield

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Record linkage, measurement error, parametric bootstrap.