This paper develops a bias correction scheme for reparametrized inverse gamma regression models with varying precision [Bourguignon M, Gallardo DI. Reparametrized inverse gamma regression models with varying precision. Stat Neerl. 2020;74(4):611–627], which is tailored to situations where the response variable has an asymmetrical shape on the positive real line. In particular, we discuss maximum-likelihood estimation for the model parameters and derive closed-form expressions for the first-order bias of the estimators. The expressions derived are simple and only require the definition of a few matrices. This enables us to obtain corrected estimators that are approximately unbiased. We conduct an extensive Monte Carlo simulation study to evaluate the performance of the proposed corrected estimators. Finally, we apply the results obtained in three real-world datasets. This paper contains Supplementary Material.

本文为具有不同精度的重新参数化逆伽马回归模型开发了一种偏差校正方案 [Bourguignon M, Gallardo DI。具有不同精度的重新参数化逆伽马回归模型。斯塔尼尔。2020;74(4):611–627]，适用于响应变量在正实线上具有不对称形状的情况。特别是，我们讨论了模型参数的最大似然估计，并推导出了估计器一阶偏差的封闭式表达式。导出的表达式很简单，只需要定义几个矩阵。这使我们能够获得近似无偏的校正估计量。我们进行了广泛的蒙特卡罗模拟研究，以评估建议的校正估计器的性能。最后，我们应用在三个真实世界数据集中获得的结果。本文包含补充材料。