Sparser Ordinal Regression Models Based on Parametric and Additive Location‐Shift Approaches
研究了位置偏移模型在有序回归中的应用，提出加性位置偏移模型，比非比例优势模型更简洁，比比例优势模型更灵活，并提供了R包。
Summary 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.