A unifying framework for specifying generalized linear models for categorical data

Peyhardi Jean, Trottier Catherine, Guédon Yann. 2013. A unifying framework for specifying generalized linear models for categorical data. In : Proceedings of the 28th International Workshop on Statistical Modelling (IWSM 2013), 08-12 July 2013, Palermo, Italy. Vol. 1. Eds. V. Muggeo, V. Capursi, G. Boscaino, G. Lovison. Amsterdam : Statistical Modelling Society, 331-335. ISBN 978-88-96251-47-8 International Workshop on Statistical Modelling. 28, Palermo, Italie, 8 Août 2013/12 Août 2013.

Résumé : In the context of categorical data analysis, the case of nominal and ordinal data has been investigated in depth while the case of partially ordered data has been comparatively neglected. We first propose a new specification of generalized linear models (GLMs) for categorical response variables which encompasses all the classical models such as multinomial logit, odds proportional or continuation ratio models but also led us to identify new GLMs. This unifying framework makes the different GLMs easier to compare and combine. We then define the more general class of partitioned conditional GLMs for categorical response variables. This new class enables to take into account the case of partially ordered data by combining nominal and ordinal GLMs.

Classification Agris : U10 - Informatique, mathématiques et statistiques

Auteurs et affiliations

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

Source : Cirad - Agritrop (https://agritrop.cirad.fr/571676/)

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