发表机构
WU Vienna University of Economics and Business（维也纳经济大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对分类响应模型中分离现象导致极大似然估计不存在或不唯一的问题，基于结构向量刻画，介绍R包divoRce，提供全面的分离诊断、类型区分及变量识别功能，并建议将其纳入常规诊断流程。
AI 中文摘要
在分类响应（包括二元、名义和有序响应）的统计模型中，可能会出现一种称为“分离”的现象，该现象可能导致最大似然估计不存在和/或不唯一。基于Sablica等人（2026年）通过结构向量对分离现象的一般性刻画，本文讨论了不同软件包中分离现象的计算、数值和实践方面，包括其后果、补救措施以及诊断方法。我们进一步介绍了R包divoRce，它提供了现存最全面的分离诊断软件套件。该包能够检查分离，区分分离类型，刻画衰退锥，并识别几乎所有文献中的分类响应模型（包括具有基线类别链接、累积链接、相邻类别链接和顺序链接设定的模型）中导致分离的变量和观测。诊断基于计算几何和线性规划的方法，可在浮点或有理算术中使用不同的求解器进行。该功能可通过包装器和S3方法轻松扩展，以适应第三方包中的实现或新的分类响应模型。我们详细讨论了我们的实现，并在广泛的应用中展示了我们的软件的实际运行情况。我们建议将我们的分离和MLE存在性检查作为每个分类数据分析中的诊断常规，既可用于第三方软件实现，也可作为标准模型诊断工作流程的一部分。
英文摘要
In statistical models for categorical responses, including binary, nominal and ordinal responses, a phenomenon called ``separation'' may occur which can render maximum likelihood estimate nonexistent and/or nonunique. Based on Sablica et al. (2026)'s general characterization of separation phenomena via structure vectors, in this article we discuss computational, numerical and practical aspects of separation phenomena in different software packages, including consequences, remedies and ways of diagnosing them. We further introduce the R package divoRce which provides the most comprehensive software suite to diagnose separation in existence. It allows to check for separation, to distinguish between types of separation, characterize the recession cone and identify variables and observations contributing to separation for almost any categorical response model in the literature, including models with baseline-category link, cumulative link, adjacent-category link and sequential link specifications. Diagnostics are based on methods for computational geometry and linear programming and can be done in floating-point or rational arithmetic with different solvers. The functionality is easily extendible by wrappers and S3 methods to accommodate implementations in third-party packages or of new categorical response models. We discuss our implementation in detail and show our software in action in a wide range of applications. We recommend to incorporate our checks for separation and existence of the MLE as a diagnostic routine in every categorical data analysis both in third-party software implementations and as part of a standard model diagnostic workflow.
Comments49 pages, 4 figures
