Semi-parametric estimation of Pearson correlation coefficient under additive distortion measurement errors

Jun Zhang and Bingqing Lin

Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 18, 5806-5829

Abstract: In this article, we investigate the estimation of the Pearson correlation coefficient in the present of additive distortion measurement errors, which are influenced by a shared, observed confounding variable. We introduce two estimators for the Pearson correlation coefficient: the profile least squares estimator and the moment-based estimator. Notably, these estimators do not rely on the assumption of independence between the confounding variable and the underlying variables. Furthermore, we demonstrate that the proposed estimators are asymptotically efficient. To evaluate their performance, we conduct comparisons between our proposed estimators and existing methods found in the literature through simulation studies. Additionally, we apply these methodologies to analyze a real dataset as an illustrative example.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2024.2446419 (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:lstaxx:v:54:y:2025:i:18:p:5806-5829

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2024.2446419

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().