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Partitioned conditional generalized linear models for categorical data

Jean Peyhardi 1, 2, * Catherine Trottier 2, 3 Yann Guédon 4, 1 
* Corresponding author
1 VIRTUAL PLANTS - Modeling plant morphogenesis at different scales, from genes to phenotype
CRISAM - Inria Sophia Antipolis - Méditerranée , INRA - Institut National de la Recherche Agronomique, UMR AGAP - Amélioration génétique et adaptation des plantes méditerranéennes et tropicales
Abstract : In categorical data analysis, several regression models have been pro-posed 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 lin-ear 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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Submitted on : Wednesday, November 19, 2014 - 2:38:48 PM
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  • HAL Id : hal-01084505, version 1
  • PRODINRA : 313847


Jean Peyhardi, Catherine Trottier, Yann Guédon. Partitioned conditional generalized linear models for categorical data. 29th International Workshop on Statistical Modelling (IWSM 2014), Statistical Modelling Society, Jul 2014, Göttingen, Germany. 4 p. ⟨hal-01084505⟩



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