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

https://hal.inria.fr/hal-01101036
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

File

1405.5802v1.pdf
Files produced by the author(s)

Identifiers

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

Citation

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

Share

Metrics

Record views

505

Files downloads

221