Bootstrap Liu estimators for Poisson regression model

The Liu estimator is used to get precise estimatesby introducing bootstrap technique to reduce the problem of multicollinearity in Poisson regression model. In the presence of multicollinearity, the variance of maximum likelihood estimator (MLE) becomes overstated and theinference based on MLEdoes not remain trustworthy. In this article, we proposed some new Poisson bootstrap Liu and ridge estimators. The proposed estimators are then compared with the non-bootstrap Liu and ridge estimators. Based on mean-squared error criterion, the simulation study revealed showed that the proposed estimators showed efficient results as compared to other existing estimators. Finally, a real example is used to illustrate the application ofproposed estimators.

Automated summary

The Liu estimator is introduced to tackle the issue of multicollinearity in Poisson regression model by utilizing bootstrap technique. This article proposes new Poisson bootstrap Liu and ridge estimators and compares them with the non-bootstrap Liu and ridge estimators. The simulation study shows that the proposed estimators demonstrate better efficiency compared to existing estimators. Additionally, a real example is used to illustrate the application of the proposed estimators.