The Poisson regression model (PRM) is employed in modelling the relationship between a count variable (y) and one or more explanatory variables. The parameters of PRM are popularly estimated using the Poisson maximum likelihood estimator (PMLE). There is a tendency that the explanatory variables grow together, which results in the problem of multicollinearity. The variance of the PMLE becomes inflated in the presence of multicollinearity. The Poisson ridge regression (PRRE) and Liu estimator (PLE) have been suggested as an alternative to the PMLE. However, in this study, we propose a new estimator to estimate the regression coefficients for the PRM when multicollinearity is a challenge. We perform a simulation study under different specifications to assess the performance of the new estimator and the existing ones. The performance was evaluated using the scalar mean square error criterion and the mean squared error prediction error. The aircraft damage data was adopted for the application study and the estimators’ performance judged by the SMSE and the mean squared prediction error. The theoretical comparison shows that the proposed estimator outperforms other estimators. This is further supported by the simulation study and the application result.

泊松回归模型 (PRM) 用于对计数变量 (y) 与一个或多个解释变量之间的关系进行建模。PRM 的参数通常使用泊松最大似然估计器 (PMLE) 进行估计。存在解释变量一起增长的趋势，这导致了多重共线性问题。在存在多重共线性的情况下，PMLE 的方差变得膨胀。泊松岭回归 (PRRE) 和 Liu 估计量 (PLE) 已被建议作为 PMLE 的替代方案。然而，在这项研究中，我们提出了一个新的估计器来估计当多重共线性是一个挑战时 PRM 的回归系数。我们在不同规格下进行模拟研究，以评估新估计器和现有估计器的性能。使用标量均方误差标准和均方误差预测误差评估性能。应用研究采用飞机损坏数据，并通过SMSE和均方预测误差判断估计器的性能。理论比较表明，所提出的估计器优于其他估计器。模拟研究和应用结果进一步支持了这一点。