Månsson and Shukur (Econ Model 28:1475–1481, 2011) proposed a Poisson ridge regression estimator (PRRE) to reduce the negative effects of multicollinearity. However, a weakness of the PRRE is its relatively large bias. Therefore, as a remedy, Türkan and Özel (J Appl Stat 43:1892–1905, 2016) examined the performance of almost unbiased ridge estimators for the Poisson regression model. These estimators will not only reduce the consequences of multicollinearity but also decrease the bias of PRRE and thus perform more efficiently. The aim of this paper is twofold. Firstly, to derive the mean square error properties of the Modified Almost Unbiased PRRE (MAUPRRE) and Almost Unbiased PRRE (AUPRRE) and then propose new ridge estimators for MAUPRRE and AUPRRE. Secondly, to compare the performance of the MAUPRRE with the AUPRRE, PRRE and maximum likelihood estimator. Using both simulation study and real-world dataset from the Swedish football league, it is evidenced that one of the proposed, MAUPRRE (\( \hat{k}_{q4} \)) performed better than the rest in the presence of high to strong (0.80–0.99) multicollinearity situation.

Månsson 和 Shukur (Econ Model 28:1475–1481, 2011) 提出了泊松岭回归估计器 (PRRE) 以减少多重共线性的负面影响。然而，PRRE 的一个弱点是其相对较大的偏差。因此，作为一种补救措施，Türkan 和 Özel（J Appl Stat 43:1892–1905, 2016）检查了泊松回归模型中几乎无偏的岭估计器的性能。这些估计器不仅会减少多重共线性的后果，还会减少 PRRE 的偏差，从而更有效地执行。本文的目的是双重的。首先，导出改进的几乎无偏 PRRE (MAUPRRE) 和几乎无偏 PRRE (AUPRRE) 的均方误差属性，然后为 MAUPRRE 和 AUPRRE 提出新的岭估计量。其次，比较 MAUPRRE 和 AUPRRE 的性能，PRRE 和最大似然估计。使用瑞典足球联赛的模拟研究和真实世界数据集，可以证明其中一个提议的 MAUPRRE (\( \hat{k}_{q4} \) ) 在存在从高到强 (0.80–0.99) 多重共线性情况下的表现优于其他。