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Overlapped We Stand, Separated We Fall: Software Approaches to Diagnosing Separation Phenomena in Categorical Response Models — arXiv:2610.09951

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.09951
License (as archived): CC0-1.0
Archived: 2026-10-08T12:40:23.385Z
SHA-256 of the archived text: a081ed3d280aafd09318d5e0c8a69f0c37e820f68c41b563e371fa9e47698244
Stable URL of this page: https://metraipolis.com/documento/dc_4e58eb87cc7a

How to cite

Overlapped We Stand, Separated We Fall: Software Approaches to Diagnosing Separation Phenomena in Categorical Response Models — arXiv:2610.09951 — arXiv (en), https://arxiv.org/abs/2610.09951 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4e58eb87cc7a (Metraipolis archive dc_4e58eb87cc7a, sha256 a081ed3d280aafd09318d5e0c8a69f0c37e820f68c41b563e371fa9e47698244)

BibTeX

@misc{dc4e58eb87cc7a,
  title = {Overlapped We Stand, Separated We Fall: Software Approaches to Diagnosing Separation Phenomena in Categorical Response Models — arXiv:2610.09951},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.09951},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4e58eb87cc7a; sha256 a081ed3d280aafd09318d5e0c8a69f0c37e820f68c41b563e371fa9e47698244},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.09951. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.AP, nuovi lavori del giorno).
Categorie arXiv: Computation (stat.CO) ; Applications (stat.AP).
Pagina del lavoro: https://arxiv.org/abs/2610.09951 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T12:15:30.234Z dalla pagina https://arxiv.org/list/stat.AP/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).