A new general biased estimator in linear measurement error model
Pragya Goyal,
Manoj K. Tiwari,
Vikas Bist and
Faisal Ababneh
Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 10, 2827-2843
Abstract:
Numerous biased estimators are known to circumvent the multicollinearity problem in linear measurement error models. This article proposes a general biased estimator with the ridge regression and the Liu estimators as special cases. The efficiency of the suggested estimator is compared with ridge regression and Liu estimators under the mean squared error matrix criterion. In addition, a Monte Carlo simulation study and a numerical evaluation have been conducted to elucidate the superiority of the new general biased estimator over other estimators.
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2024.2376667 (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:10:p:2827-2843
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20
DOI: 10.1080/03610926.2024.2376667
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 ().