Score test for assessing the conditional dependence in latent class models and its application to record linkage

Huiping Xu, Xiaochun Li, Zuoyi Zhang and Shaun Grannis

Journal of the Royal Statistical Society Series C, 2022, vol. 71, issue 5, 1663-1687

Abstract: The Fellegi–Sunter model has been widely used in probabilistic record linkage despite its often invalid conditional independence assumption. Prior research has demonstrated that conditional dependence latent class models yield improved match performance when using the correct conditional dependence structure. With a misspecified conditional dependence structure, these models can yield worse performance. It is, therefore, critically important to correctly identify the conditional dependence structure. Existing methods for identifying the conditional dependence structure include the correlation residual plot, the log‐odds ratio check, and the bivariate residual, all of which have been shown to perform inadequately. Bootstrap bivariate residual approach and score test have also been proposed and found to have better performance, with the score test having greater power and lower computational burden. In this paper, we extend the score‐test‐based approach to account for different conditional dependence structures. Through a simulation study, we develop practical recommendations on the utilisation of the score test and assess the match performance with conditional dependence identified by the proposed method. Performance of the proposed method is further evaluated using a real‐world record linkage example. Findings show that the proposed method leads to improved matching accuracy relative to the Fellegi–Sunter model.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/rssc.12590

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:71:y:2022:i:5:p:1663-1687

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().