Abstract
Ridge estimator in linear regression model requires a ridge parameter, K, of which many have been proposed. In this study, estimators based on Dorugade (2014) and Adnan et al. (2014) were classified into different forms and various types using the idea of Lukman and Ayinde (2015). Some new ridge estimators were proposed. Results shows that the proposed estimators based on Adnan et al. (2014) perform generally better than the existing ones.
DOI
10.22237/jmasm/1493598240
Recommended Citation
Lukman, A. F., Ayinde, K., & Ajiboye, A. S. (2017). Monte Carlo study of some classification-based ridge parameter estimators. Journal of Modern Applied Statistical Methods, 16(1), 428-451. doi: 10.22237/jmasm/1493598240
Included in
Applied Statistics Commons, Social and Behavioral Sciences Commons, Statistical Theory Commons
