A Solution to Separation in Binary Response Models
A Solution to Separation in Binary Response Models
复制标题
二元响应模型中的分离解决方案
DOI：
10.1093/pan/mpi009
复制
发表时间：
2005
影响因子：
5.4
通讯作者：
Christopher Zorn
中科院分区：
文献类型：
--
作者：
Christopher Zorn
二分因变量模型中的一个常见问题是“分离”，当一个模型的一个或多个协变量完美地预测一些二元结果时，就会发生这种情况。分离提出了一系列特别困难的问题，通常迫使研究人员在忽略明显重要的协变量和进行事后数据或估计校正之间做出选择。在这篇文章中，我提出了一种解决分离问题的方法，基于惩罚似然校正标准二项式GLM评分函数。然后，我将这种方法应用于一项关于战后领导人命运的重要研究中的数据。
A common problem in models for dichotomous dependent variables is “separation,” which occurs when one or more of a model's covariates perfectly predict some binary outcome. Separation raises a particularly difficult set of issues, often forcing researchers to choose between omitting clearly important covariates and undertaking post—hoc data or estimation corrections. In this article I present a method for solving the separation problem, based on a penalized likelihood correction to the standard binomial GLM score function. I then apply this method to data from an important study on the postwar fate of leaders.