论文标题
累积链路模型中的中位偏差减少
Median bias reduction in cumulative link models
论文作者
论文摘要
本文基于Kenne Pagui等人开发的中位偏差减少，为累积链路模型提供了一种新颖的估计方法。 （2017）。基于分数的调整，获得了中位偏差减少估计器作为估计方程的解决方案。它允许获得最大似然估计的高阶中间位置，而无需其有限。此外，估计量在组成部分单调重新聚体下是均衡的，该方法可有效预防边界估计。我们通过模拟研究评估了中位偏差的性质减少估计器，并将其与两个主要竞争者进行比较，最大可能性和平均偏差降低（Firth，1993）估计器。最后，我们展示了一个应用程序，其中提出的估计器能够解决边界估计问题。
This paper presents a novel estimation approach for cumulative link models, based on median bias reduction as developed in Kenne Pagui et al. (2017). The median bias reduced estimator is obtained as solution of an estimating equation based on an adjustment of the score. It allows to obtain higher-order median centering of maximum likelihood estimates without requiring their finiteness. Moreover, the estimator is equivariant under componentwise monotone reparameterizations and the method is effective in preventing boundary estimates. We evaluate the properties of the median bias reduced estimator through simulation studies and compare it with the two main competitors, the maximum likelihood and the mean bias reduced (Firth, 1993) estimators. Finally, we show an application where the proposed estimator is able to solve the boundary estimates problem.
