Unbiased Modified Two-Parameter Estimator for the Linear Regression Model
This study centers on estimating parameters in a linear regression model in the presence of multicollinearity. Multicollinearity poses a threat to the efficiency of the Ordinary Least Squares (OLS) estimator. Some alternative estimators have been developed as remedial measures to the earlier mentioned problem. This study introduces a new unbiased modified two-parameter estimator based on prior information. Its properties are also considered; the new estimator was compared with other estimators’ Mean Square Error (MSE). A numerical example and Monte Carlo simulation were used to illustrate the performance of the new estimator.
How to Cite
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
© Journal of Scientific Research
Articles published in the "Journal of Scientific Research" are Open Access articles under a Creative Commons Attribution-ShareAlike 4.0 International license (CC BY-SA 4.0). This license permits use, distribution and reproduction in any medium, provided the original work is properly cited and initial publication in this journal. In addition to that, users must provide a link to the license, indicate if changes are made and distribute using the same license as original if the original content has been remixed, transformed or built upon.