Abstract

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.

摘要

Liu估计器通过引入bootstrap技术来减少泊松回归模型中的多重共线性问题，从而获得精确的估计。在存在多重共线性的情况下，最大似然估计量 (MLE) 的方差会被夸大，并且基于 MLE 的推论不再可靠。在本文中，我们提出了一些新的泊松引导刘和岭估计器。然后将所提出的估计器与非自举刘和岭估计器进行比较。基于均方误差准则，模拟研究表明，与其他现有估计器相比，所提出的估计器显示出有效的结果。最后，用一个真实的例子来说明所提出的估计器的应用。