Modified Jackknifed Ridge Estimator in Bell Regression Model: Theory, Simulation and Applications

Authors

  • Zakariya Algamal University of Mosul
  • Adewale Lukman University of Medical Sciences
  • B. M. Kibria Golam Florida International University
  • Arowolo Taofik Lagos State University of Technology

DOI:

https://doi.org/10.52866/ijcsm.2023.01.01.0012

Keywords:

Bell regression model, Jackknife estimator, Multicollinearity

Abstract

Regression models explore the relationship between the response variable and one or more explanatory variables. It becomes practically challenging in real-life applications to model this relationship when the explanatory variables are linearly dependent. Conventionally, to avoid this issue, several shrinkage estimators are proposed. The ridge estimator is one of the most popular methods of estimation when there is linear dependency among the explanatory variables. In this study, we proposed a jackknifed version of the ridge estimator for the Bell regression model by jackknifing the ridge estimator to reduce the biasedness. The simulation and the application results revealed that the proposed estimator enhance the performance of the ridge estimator.

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Published

2023-01-07

How to Cite

[1]
Z. Algamal, A. Lukman, B. M. K. . Golam, and A. . Taofik, “Modified Jackknifed Ridge Estimator in Bell Regression Model: Theory, Simulation and Applications”, Iraqi Journal For Computer Science and Mathematics, vol. 4, no. 1, pp. 146–154, Jan. 2023.
CITATION
DOI: 10.52866/ijcsm.2023.01.01.0012
Published: 2023-01-07

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Section

Articles