A Modified Two Parameter Estimator with Different Forms of Biasing Parameters in the Linear Regression Model

• Published in: African Scientific Reports pp. 212 - 228
• Main Authors: Owolabi, Abiola T., Ayinde, Kayode, Alabi, Olusegun O.
• Format: Journal Article
• Language: English
• Published: Nigerian Society of Physical Sciences 29-12-2022
• Subjects: ridge estimator, liu estimator, multicollinearity, mean square error, kibria-lukman estimator, prior iinformation
• ISSN: 2955-1625; 2955-1617
• DOI: 10.46481/asr.2022.1.3.62

Despite its common usage in estimating the linear regression model parameters, the ordinary least squares estimator often suffers a breakdown when two or more predictor variables are strongly correlated. This study proposes an alternative estimator to the OLS and other existing ridge-type estimators to tackle the problem of correlated regressors (multicollinearity). The properties of the proposed estimator were derived, and six forms of biasing parameter k (generalized, median, mid-range, arithmetic, harmonic and geometric means) were used in the proposed estimator to compare its performance with five other existing estimators through a simulation study. The proposed estimator dominated existing estimators when the mid-range, arithmetic mean, and median versions of k were used. However, the proposed estimator did not perform well when the generalized, harmonic, and geometricmean versions were used.