Partitioned conditional generalized linear models for categorical data

2014/05/22 by Jean Peyhardi, Peyhardi, Jean, Catherine Trottier +3
Agricultural and Biological Sciences · Computer Science · Decision Sciences · Mathematics · #Artificial intelligence #Bayesian Modeling and Causal Inference #Categorical variable #Class (philosophy) #Combinatorics #Computer science #FOS: Computer and information sciences #Generalized linear model #Hierarchy #Mathematics #Methodology (stat.ME) #Multi-Criteria Decision Making #Ordinal data #Ordinal regression #Partition (number theory) #Sensory Analysis and Statistical Methods #Statistics #stat.ME

paper · pdf · doi:10.48550/arxiv.1405.5802

25 pages, 13 figures

arxiv created 2014/05/22 · openalex publication_date 2014/05/22 · arxiv updated 2014/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

In categorical data analysis, several regression models have been proposed for hierarchically-structured response variables, e.g. the nested logit model. But they have been formally defined for only two or three levels in the hierarchy. Here, we introduce the class of partitioned conditional generalized linear models (PCGLMs) defined for any numbers of levels. The hierarchical structure of these models is fully specified by a partition tree of categories. Using the genericity of the (r,F,Z) specification, the PCGLM can handle nominal, ordinal but also partially-ordered response variables.

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