Unbiased Modified Two-Parameter Estimator for the Linear Regression Model

Authors

  • A. O. Abidoye Department of Statistics, Univesity of Ilorin, Ilorin, Kwara State, Nigeria
  • I. M. Ajayi Department of Social and Physical Sciences, Landmark University, Omu-Aran, Kwara State, Nigeria
  • F. L. Adewale Department of Social and Physical Sciences, Landmark University, Omu-Aran, Kwara State, Nigeria
  • J. O. Ogunjobi Department of Social and Physical Sciences, Landmark University, Omu-Aran, Kwara State, Nigeria

DOI:

https://doi.org/10.3329/jsr.v14i3.58234

Abstract

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.

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Published

2022-09-01

How to Cite

Abidoye, A. O., Ajayi, I. M. ., Adewale, F. L., & Ogunjobi, J. O. (2022). Unbiased Modified Two-Parameter Estimator for the Linear Regression Model. Journal of Scientific Research, 14(3), 785–795. https://doi.org/10.3329/jsr.v14i3.58234

Issue

Vol. 14 No. 3 (2022)

Section

Section A: Physical and Mathematical Sciences

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

© Journal of Scientific Research

Creative Commons License

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.