We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Partitioned conditional generalized linear models for categorical responses.
- Authors
Peyhardi, Jean; Trottier, Catherine; Guédon, Yann
- Abstract
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 <named-content>(r,F,Z)</named-content> 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.
- Subjects
LINEAR statistical models; DATA analysis; CATEGORIES (Mathematics); REGRESSION analysis; ORGANIZATIONAL structure
- Publication
Statistical Modelling: An International Journal, 2016, Vol 16, Issue 4, p297
- ISSN
1471-082X
- Publication type
Article
- DOI
10.1177/1471082X16644874