Sparser Ordinal Regression Models Based on Parametric and Additive Location‐Shift Approaches

Gerhard Tutz and Moritz Berger

International Statistical Review, 2022, vol. 90, issue 2, 306-327

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 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.

Date: 2022
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