Partitioned conditional generalized linear models for categorical data

Peyhardi Jean, Trottier Catherine, Guédon Yann. 2014. Partitioned conditional generalized linear models for categorical data. In : Proceedings of the 29th International Workshop on Statistical Modelling, Volume 1, July 14-18, 2013, Göttingen, Germany. Eds. Thomas Kneib, Fabian Sobotka, Jan Fahrenholz, Henriette Irmer. Göttingen : Centre for Statistics, 269-272. International Workshop on Statistical Modelling. 29, Göttingen, Allemagne, 14 Juillet 2014/18 Juillet 2014.

Résumé : 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.

Classification Agris : U10 - Informatique, mathématiques et statistiques

Auteurs et affiliations

• Peyhardi Jean, UM2 (FRA)
• Trottier Catherine, UM2 (FRA)
• Guédon Yann, CIRAD-BIOS-UMR AGAP (FRA)

Source : Cirad - Agritrop (https://agritrop.cirad.fr/573967/)

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