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- W4313294559 abstract "The most popularly used estimator to estimate the regression parameters in the linear regression model is the ordinary least-squares (OLS). The existence of multicollinearity in the model renders OLS inefficient. To overcome the multicollinearity problem, a new two-parameter estimator, a biased two-parameter (BTP), is proposed as an alternative to the OLS. Theoretical comparisons and simulation studies were carried out. The theoretical comparison and simulation studies show that the proposed estimator dominated some existing estimators using the mean square error (MSE) criterion. Furthermore, the real-life data bolster both the hypothetical and simulation results. The proposed estimator is preferred to OLS and other existing estimators when multicollinearity is present in the model." @default.
- W4313294559 created "2023-01-06" @default.
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- W4313294559 date "2022-12-29" @default.
- W4313294559 modified "2023-10-13" @default.
- W4313294559 title "On the biased Two-Parameter Estimator to Combat Multicollinearity in Linear Regression Model" @default.
- W4313294559 doi "https://doi.org/10.46481/asr.2022.1.3.57" @default.
- W4313294559 hasPublicationYear "2022" @default.
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