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Sparser Ordinal Regression Models Based on Parametric and Additive Location-Shift Approaches
[journal article]
Abstract The potential of location-shift models to find adequate models between the proportional odds model and the non-proportional odds model is investigated. It is demonstrated that these models are very useful in ordinal modelling. While proportional odds models are often too simple, non-proportional odd... view more
The potential of location-shift models to find adequate models between the proportional odds model and the non-proportional odds model is investigated. It is demonstrated that these models are very useful in ordinal modelling. While proportional odds models are often too simple, non-proportional odds models are typically unnecessary complicated and seem widely dispensable. In addition, the class of location-shift models is extended to allow for smooth effects. The additive location-shift model contains two functions for each explanatory variable, one for the location and one for dispersion. It is much sparser than hard-to-handle additive models with category-specific covariate functions but more flexible than common vector generalised additive models. An R package is provided that is able to fit parametric and additive location-shift models.... view less
Keywords
regression; model; statistics
Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
adjacent categories model; cumulative model; dispersion; location-shift model; ordinal regression; proportional odds model; Vorwahl-Querschnitt (GLES 2013) (ZA5700 v2.0.0)
Document language
English
Publication Year
2022
Page/Pages
p. 306-327
Journal
International Statistical Review, 90 (2022) 2
DOI
https://doi.org/10.1111/insr.12484
ISSN
1751-5823
Status
Published Version; peer reviewed