Jackknife Kibria-Lukman estimator for the beta regression model

Tuba Koç and Emre Dünder

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 21, 7789-7805

Abstract: The beta regression model is a flexible model, which widely used when the dependent variable is in ratios and percentages in the range of (0.1). The coefficients of the beta regression model are estimated using the maximum likelihood method. In cases where there is a multicollinearity problem, the use of maximum likelihood (ML) leads to problems such as inconsistent parameter estimates and inflated variance.In the presence of multicollinearity, the use of maximum likelihood (ML) leads to problems such as inconsistent parameter estimates and inflated variance. In this study, KL estimator and its jackknifed version are proposed to reduce the effects of multicollinearity in the beta regression model. The performance of the proposed jackknifed KL beta regression estimator is compared with ridge, Liu and KL estimators through simulation studies and real data applications. The results show that the proposed estimators mostly outperform ML, ridge, Liu and KL estimators.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2023.2273206 (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:53:y:2024:i:21:p:7789-7805

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

DOI: 10.1080/03610926.2023.2273206

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 ().