Further research on the modified ridge principal component estimator in linear model
Hongmei Chen,
Jibo Wu and
B. M. Golam Kibria
Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 24, 8894-8901
Abstract:
Yang and Huang have introduced a generalized Mahalanobis loss function which can be applied to the estimator for regression coefficients whether its covariance matrix is singular or non singular. In this paper, we give the detailed comparisons among those estimators that can be derived from the modified ridge principal component estimator under the generalized Mahalanobis loss function by the average loss criterion. Also, we obtain conditions for the superiority of one estimator over the others. Furthermore, two numerical examples are given to illustrate the theoretical results.
Date: 2023
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2022.2085876 (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:52:y:2023:i:24:p:8894-8901
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20
DOI: 10.1080/03610926.2022.2085876
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 ().