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  • 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. (xsd:string)
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  • (GLES) (xsd:string)
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  • GLES-Bibliography (xsd:string)
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  • 12. Fassung, Dezember 2022 (xsd:gyear)
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  • 2022 (xsd:gyear)
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  • 2022 (xsd:gyear)
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  • 10.1111/insr.12484 ()
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  • 1 (xsd:string)
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  • 03067734 ()
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  • Sparser Ordinal Regression Models Based on Parametric and Additive Location-Shift Approaches (xsd:string)
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  • In International Statistical Review, 1-22, 2022 (xsd:string)
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  • German Longitudinal Election Study (GLES) (xsd:string)
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  • 2022 (xsd:string)
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  • 22 (xsd:string)
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  • 22.12.2022 (xsd:gyear)
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