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Ordinal regression: A review and a taxonomy of models
[journal article]
Abstract Ordinal models can be seen as being composed from simpler, in particular binary models. This view on ordinal models allows to derive a taxonomy of models that includes basic ordinal regression models, models with more complex parameterizations, the class of hierarchically structured models, and the ... view more
Ordinal models can be seen as being composed from simpler, in particular binary models. This view on ordinal models allows to derive a taxonomy of models that includes basic ordinal regression models, models with more complex parameterizations, the class of hierarchically structured models, and the more recently developed finite mixture models. The structured overview that is given covers existing models and shows how models can be extended to account for further effects of explanatory variables. Particular attention is given to the modeling of additional heterogeneity as, for example, dispersion effects. The modeling is embedded into the framework of response styles and the exact meaning of heterogeneity terms in ordinal models is investigated. It is shown that the meaning of terms is crucially determined by the type of model that is used. Moreover, it is demonstrated how models with a complex category-specific effect structure can be simplified to obtain simpler models that fit sufficiently well. The fitting of models is illustrated by use of a real data set, and a short overview of existing software is given.... view less
Keywords
regression analysis; model; statistics; statistical method
Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
adjacent categories model; cumulative model; hierarchically structured models; ordinal regression; proportional odds model; sequential model; ZA5700: Pre-election Cross Section (GLES 2013) (Data file Version 2.0.0)
Document language
English
Publication Year
2022
Page/Pages
p. 1-28
Journal
WIREs Computational Statistics, 14 (2022) 2
DOI
https://doi.org/10.1002/wics.1545
ISSN
1939-0068
Status
Published Version; peer reviewed
Licence
Creative Commons - Attribution-Noncommercial-No Derivative Works 4.0