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An Unbiased Convex Estimator for Classical Linear Regression Model Using Prior Information

Abstract Number:

2298 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Mustafa I. Alheety (1), HM Nayem (2), B M Golam Kibria (3)

Institutions:

(1) University of Anbar, Anbar, Iraq, (2) N/A, N/A, (3) Florida International University, Miami, FL

Co-Author(s):

HM Nayem  
N/A
B M Golam Kibria  
Florida International University

First Author:

Mustafa I. Alheety  
University of Anbar

Presenting Author:

HM Nayem  
N/A

Abstract Text:

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|Unbiased ridge estimator|Restricted least-squares estimator|Multicollinearity|

Sponsors:

Section on Statistical Computing

Tracks:

Monte Carlo Methods & Simulation

Can this be considered for alternate subtype?

No

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I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 3, 2025. The registration fee is non-refundable.

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