Surveys in Mathematics and its Applications

ISSN 1842-6298 (electronic), 1843-7265 (print)
 Volume 18 (2023), 183 -- 221

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

SOME ONE AND TWO PARAMETER ESTIMATORS FOR THE MULTICOLLINEAR GAUSSIAN LINEAR REGRESSION MODEL: SIMULATIONS AND APPLICATIONS

Md Ariful Hoque and B. M. Golam Kibria

Abstract. The ordinary least square estimator is inefficient when there exists multicollinearity among regressors in linear regression model. There are many methods available in literature to solve the multicollinearity problem. In this study, we consider some one and two parameter estimators for estimating the regression parameters. We theoretically compared the estimators in terms of smaller mean squared error (MSE) criteria. A Monte Carlo simulation study has been conducted to compare the performance of the estimators numerically. Finally, for illustration purposes, a real-life data is analyzed.

2020 Mathematics Subject Classification: 62J07; 62F10.
Keywords: D estimator; Linear Regression Model; MSE; Multicollinearity; Ridge Regression estimator; James-Stein Estimator; Liu estimator; Modified Liu estimator; Simulation Study.

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Md Ariful Hoque
Department of Mathematics and Statistics,
Florida International University, Miami, Florida, USA.
e-mail: mhoqu010@fiu.edu

B. M. Golam Kibria
Department of Mathematics and Statistics,
Florida International University, Miami, Florida, USA.
e-mail: kibriag@fiu.edu

http://www.utgjiu.ro/math/sma