Jackknifed K-L estimator in Bell regression model

Authors

  • Luay Adil Abduljabbar, Zakariya Yahya Algamal

Keywords:

Collinearity; K-L estimator; Bell regression model; Jackknife estimator; Monte Carlo simulation.

Abstract

When there is collinearity between the response variable and numerous explanatory variables, modeling the link between the response variable and several explanatory variables is difficult. Several shrinkage estimators have traditionally been presented to avoid this problem. The Kibria and Lukman estimator is one of them (K-L). In this paper, a jackknifed version of the K-L estimator in the Bell regression model is proposed, which combines the Jackknife process with the K-L estimator to reduce biasedness. In terms of absolute bias and mean squared error, our Monte Carlo simulation findings and real-world application of the Bell regression model imply that the suggested estimate can provide significant improvements over current competing estimators.

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Published

2022-07-23