Domain estimation under informative linkage

Ray Chambers, Nicola Salvati, Enrico Fabrizi and Andrea Diniz da Silva

Statistical Theory and Related Fields, 2019, vol. 3, issue 2, 90-102

Abstract: A standard assumption when modelling linked sample data is that the stochastic properties of the linking process and process underpinning the population values of the response variable are independent of one another. This is often referred to as non-informative linkage. But what if linkage errors are informative? In this paper, we provide results from two simulation experiments that explore two potential informative linking scenarios. The first is where the choice of sample record to link is dependent on the response; and the second is where the probability of correct linkage is dependent on the response. We focus on the important and widely applicable problem of estimation of domain means given linked data, and provide empirical evidence that while standard domain estimation methods can be substantially biased in the presence of informative linkage errors, an alternative estimation method, based on a Gaussian approximation to a maximum likelihood estimator that allows for non-informative linkage error, performs well.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/24754269.2019.1653158 (text/html)
Access to full text is restricted to subscribers.

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:taf:tstfxx:v:3:y:2019:i:2:p:90-102

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tstf20

DOI: 10.1080/24754269.2019.1653158

Access Statistics for this article

Statistical Theory and Related Fields is currently edited by Zhao Wei

More articles in Statistical Theory and Related Fields from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().