An Unbiased Convex Estimator Depending on Prior Information for the Classical Linear Regression Model

Mustafa I. Alheety, Nayem Hm and B. M. Golam Kibria ()
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Mustafa I. Alheety: Department of Mathematics, College of Education for Pure Sciences, University of Anbar, Anbar 31001, Iraq
Nayem Hm: Department of Mathematics and Statistics, Florida International University, Miami, FL 33199, USA
B. M. Golam Kibria: Department of Mathematics and Statistics, Florida International University, Miami, FL 33199, USA

Stats, 2025, vol. 8, issue 1, 1-33

Abstract: We propose an unbiased restricted estimator that leverages prior information to enhance estimation efficiency for the linear regression model. The statistical properties of the proposed estimator are rigorously examined, highlighting its superiority over several existing methods. A simulation study is conducted to evaluate the performance of the estimators, and real-world data on total national research and development expenditures by country are analyzed to illustrate the findings. Both the simulation results and real-data analysis demonstrate that the proposed estimator consistently outperforms the alternatives considered in this study.

Keywords: linear model; MSE; multicollinearity; restricted least-squares estimator; unbiased ridge estimator (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2025
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