Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Partitioned conditional generalized linear models for categorical data

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.
Document type :
Preprints, Working Papers, ...
Complete list of metadata

Cited literature [14 references]  Display  Hide  Download
Contributor : Christophe Godin <>
Submitted on : Monday, January 12, 2015 - 5:33:08 PM
Last modification on : Thursday, March 4, 2021 - 3:25:12 PM
Long-term archiving on: : Monday, April 13, 2015 - 10:16:14 AM


Files produced by the author(s)


  • HAL Id : hal-01101036, version 1
  • ARXIV : 1405.5802


Jean Peyhardi, Catherine Trottier, Yann Guédon. Partitioned conditional generalized linear models for categorical data. 2015. ⟨hal-01101036⟩



Record views


Files downloads