3.8 Article

Monte Carlo Study of Some Classification-Based Ridge Parameter Estimators

Journal

JOURNAL OF MODERN APPLIED STATISTICAL METHODS
Volume 16, Issue 1, Pages 428-451

Publisher

WAYNE STATE UNIV PRESS
DOI: 10.22237/jmasm/1493598240

Keywords

linear regression model; multicollinearity; ridge estimator; mean square error

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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.

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