On the Liu estimator in the beta and Kumaraswamy regression models: A comparative study

Shima Pirmohammadi and Hamid Bidram

Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 24, 8553-8578

Abstract: Multi-collinearity among regressors and consequently ill-conditioning inflates the mean squared error (MSE) of the maximum likelihood estimator (MLE) of the parameters in a regression model. In recent years, the Liu estimator (LE) has been widely used in the literature to improve the regression models. Since in some regression models, the dependent variable follows a double bounded distribution, such as the beta and Kumaraswamy distributions, we are going to consider these two regression models in the presence of a multi-collinearity problem with investigation of their properties, characterizations, MLEs, and LEs. Finally, MSEs of LEs and MLEs are compared under various link functions, using simulation and two real data sets.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2021.1900254 (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:51:y:2022:i:24:p:8553-8578

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

DOI: 10.1080/03610926.2021.1900254

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 ().