Partitioned conditional generalized linear models for categorical responses
In categorical data analysis, several regression models have been proposed for hierarchically structured responses, such as the nested logit model, the two-step model or the partitioned conditional model for partially ordered set. The specifications of these models are heterogeneous and 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) that encompasses all these models and is defined for any number of levels in the hierarchy. The hierarchical structure of these models is fully specified by a partition tree of categories. Using the genericity of the recently introduced specification of generalized linear models (GLMs) for categorical responses, it is possible to use different link functions and explanatory variables for each partitioning step. PCGLMs thus constitute a very flexible framework for modelling hierarchically structured categorical responses including partially ordered responses.
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Subjects: | U10 - Informatique, mathématiques et statistiques, méthode statistique, modèle linéaire, arbre, classification, analyse de régression, http://aims.fao.org/aos/agrovoc/c_7377, http://aims.fao.org/aos/agrovoc/c_34040, http://aims.fao.org/aos/agrovoc/c_7887, http://aims.fao.org/aos/agrovoc/c_1653, http://aims.fao.org/aos/agrovoc/c_16335, |
Online Access: | http://agritrop.cirad.fr/581506/ http://agritrop.cirad.fr/581506/1/PeyhardiTrottierGuedon2016.pdf |
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dig-cirad-fr-5815062024-01-28T23:40:37Z http://agritrop.cirad.fr/581506/ http://agritrop.cirad.fr/581506/ Partitioned conditional generalized linear models for categorical responses. Peyhardi Jean, Trottier Catherine, Guédon Yann. 2016. Statistical Modelling, 16 (4) : 297-321.https://doi.org/10.1177/1471082X16644874 <https://doi.org/10.1177/1471082X16644874> Partitioned conditional generalized linear models for categorical responses Peyhardi, Jean Trottier, Catherine Guédon, Yann eng 2016 Statistical Modelling U10 - Informatique, mathématiques et statistiques méthode statistique modèle linéaire arbre classification analyse de régression http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_34040 http://aims.fao.org/aos/agrovoc/c_7887 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_16335 In categorical data analysis, several regression models have been proposed for hierarchically structured responses, such as the nested logit model, the two-step model or the partitioned conditional model for partially ordered set. The specifications of these models are heterogeneous and 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) that encompasses all these models and is defined for any number of levels in the hierarchy. The hierarchical structure of these models is fully specified by a partition tree of categories. Using the genericity of the recently introduced specification of generalized linear models (GLMs) for categorical responses, it is possible to use different link functions and explanatory variables for each partitioning step. PCGLMs thus constitute a very flexible framework for modelling hierarchically structured categorical responses including partially ordered responses. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/581506/1/PeyhardiTrottierGuedon2016.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1177/1471082X16644874 10.1177/1471082X16644874 info:eu-repo/semantics/altIdentifier/doi/10.1177/1471082X16644874 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1177/1471082X16644874 |
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U10 - Informatique, mathématiques et statistiques méthode statistique modèle linéaire arbre classification analyse de régression http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_34040 http://aims.fao.org/aos/agrovoc/c_7887 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_16335 U10 - Informatique, mathématiques et statistiques méthode statistique modèle linéaire arbre classification analyse de régression http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_34040 http://aims.fao.org/aos/agrovoc/c_7887 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_16335 |
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U10 - Informatique, mathématiques et statistiques méthode statistique modèle linéaire arbre classification analyse de régression http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_34040 http://aims.fao.org/aos/agrovoc/c_7887 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_16335 U10 - Informatique, mathématiques et statistiques méthode statistique modèle linéaire arbre classification analyse de régression http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_34040 http://aims.fao.org/aos/agrovoc/c_7887 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_16335 Peyhardi, Jean Trottier, Catherine Guédon, Yann Partitioned conditional generalized linear models for categorical responses |
description |
In categorical data analysis, several regression models have been proposed for hierarchically structured responses, such as the nested logit model, the two-step model or the partitioned conditional model for partially ordered set. The specifications of these models are heterogeneous and 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) that encompasses all these models and is defined for any number of levels in the hierarchy. The hierarchical structure of these models is fully specified by a partition tree of categories. Using the genericity of the recently introduced specification of generalized linear models (GLMs) for categorical responses, it is possible to use different link functions and explanatory variables for each partitioning step. PCGLMs thus constitute a very flexible framework for modelling hierarchically structured categorical responses including partially ordered responses. |
format |
article |
topic_facet |
U10 - Informatique, mathématiques et statistiques méthode statistique modèle linéaire arbre classification analyse de régression http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_34040 http://aims.fao.org/aos/agrovoc/c_7887 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_16335 |
author |
Peyhardi, Jean Trottier, Catherine Guédon, Yann |
author_facet |
Peyhardi, Jean Trottier, Catherine Guédon, Yann |
author_sort |
Peyhardi, Jean |
title |
Partitioned conditional generalized linear models for categorical responses |
title_short |
Partitioned conditional generalized linear models for categorical responses |
title_full |
Partitioned conditional generalized linear models for categorical responses |
title_fullStr |
Partitioned conditional generalized linear models for categorical responses |
title_full_unstemmed |
Partitioned conditional generalized linear models for categorical responses |
title_sort |
partitioned conditional generalized linear models for categorical responses |
url |
http://agritrop.cirad.fr/581506/ http://agritrop.cirad.fr/581506/1/PeyhardiTrottierGuedon2016.pdf |
work_keys_str_mv |
AT peyhardijean partitionedconditionalgeneralizedlinearmodelsforcategoricalresponses AT trottiercatherine partitionedconditionalgeneralizedlinearmodelsforcategoricalresponses AT guedonyann partitionedconditionalgeneralizedlinearmodelsforcategoricalresponses |
_version_ |
1792499105974452224 |