Monte Carlo study of some classification-based ridge parameter estimators

Lukman, A. F. and Ayinde, K. and Ajiboye, A. S. (2017) Monte Carlo study of some classification-based ridge parameter estimators. Journal of Modern Applied Statistical Methods, 16 (1). pp. 428-451. ISSN 1538 − 9472

[img] Text
Some Classification-Based Ridge Parameter.pdf - Published Version
Download (1MB)
Official URL: http://digitalcommons.wayne.edu/jmasm

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.Keywords:linear regression model, multicollinearity, ridge estimator, mean square error

Item Type: Article
Subjects: H Social Sciences > HA Statistics
Depositing User: Mr DIGITAL CONTENT CREATOR LMU
Date Deposited: 20 Sep 2019 12:26
Last Modified: 20 Sep 2019 12:26
URI: https://eprints.lmu.edu.ng/id/eprint/2334

Actions (login required)

View Item View Item
Landmark University Repository is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.