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

Abiola T. Owolabi; Kayode Ayinde; Olusegun O. Alabi

doi:10.46481/asr.2022.1.3.62

Authors

Abiola T. Owolabi
atowolabi@lautech.edu.ng
Kayode Ayinde
Olusegun O. Alabi

Keywords:

Ridge estimator, Liu estimator, Multicollinearity, Mean square error, Kibria-Lukman estimator, Prior iInformation

Abstract

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 geometric
mean versions were used.

Dimensions

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