Partitioned conditional generalized linear models for categorical data
In categorical data analysis, several regression models have been proposed for hierarchically-structured response variables, such as 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 an arbitrary number 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 of GLMs for categorical data, PCGLMs can handle nominal, ordinal but also partially-ordered response variables.
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| Main Authors: | , , |
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| Format: | conference_item biblioteca |
| Language: | eng |
| Published: |
Centre for Statistics
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| Subjects: | U10 - Informatique, mathématiques et statistiques, |
| Online Access: | http://agritrop.cirad.fr/573967/ http://agritrop.cirad.fr/573967/1/document_573967.pdf |
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