A New Biased Two-Parameter Estimator in Linear Regression Model

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Abstract: The most frequently used estimation technique in the linear regression model is the ordinary least squares (OLS) estimator. The presence of multicollinearity makes the technique inefficient and gives misleading results. This study proposed a new biased two-parameter estimator to deal with the multicollinearity problem. Theory and simulation results show that this estimator outperforms existing estimators considered under some conditions, according to the mean squares error (MSE) criterion. Finally, the real-life dataset illustrates the paper's findings, which agree with the theoretical and simulation results.

Equations, E-ISSN: 2944-9146, Volume 3, 2023, Art. #10

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Olasunkanmi James Oladapo, Janet Iyabo Idowu, Abiola Timothy Owolabi, Kayode Ayinde, "A New Biased Two-Parameter Estimator in Linear Regression Model," Equations, vol. 3, pp. 73-92, 2023, DOI:10.37394/232021.2023.3.10
Olasunkanmi James Oladapo, Janet Iyabo Idowu, Abiola Timothy Owolabi, Kayode Ayinde. A New Biased Two-Parameter Estimator in Linear Regression Model. Equations. 2023;3:73-92. 10.37394/232021.2023.3.10
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