Theoretical limits of microclustering for record linkage.

Published

Journal Article

There has been substantial recent interest in record linkage, where one attempts to group the records pertaining to the same entities from one or more large databases that lack unique identifiers. This can be viewed as a type of microclustering, with few observations per cluster and a very large number of clusters. We show that the problem is fundamentally hard from a theoretical perspective and, even in idealized cases, accurate entity resolution is effectively impossible unless the number of entities is small relative to the number of records and/or the separation between records from different entities is extremely large. These results suggest conservatism in interpretation of the results of record linkage, support collection of additional data to more accurately disambiguate the entities, and motivate a focus on coarser inference. For example, results from a simulation study suggest that sometimes one may obtain accurate results for population size estimation even when fine-scale entity resolution is inaccurate.

Full Text

Duke Authors

Cited Authors

  • Johndrow, JE; Lum, K; Dunson, DB

Published Date

  • June 2018

Published In

Volume / Issue

  • 105 / 2

Start / End Page

  • 431 - 446

PubMed ID

  • 29880978

Pubmed Central ID

  • 29880978

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asy003

Language

  • eng