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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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?:dateModified
  • 2022 (xsd:gyear)
?:datePublished
  • 2022 (xsd:gyear)
?:doi
  • 10.1111/insr.12484 ()
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  • true (xsd:boolean)
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  • en (xsd:string)
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?:issn
  • 1751-5823 ()
?:issueNumber
  • 2 (xsd:string)
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?:name
  • Sparser Ordinal Regression Models Based on Parametric and Additive Location-Shift Approaches (xsd:string)
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?:publicationType
  • Zeitschriftenartikel (xsd:string)
  • journal_article (en)
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  • GESIS-SSOAR (xsd:string)
  • In: International Statistical Review, 90, 2022, 2, 306-327 (xsd:string)
rdf:type
?:url
?:urn
  • urn:nbn:de:0168-ssoar-93052-8 ()
?:volumeNumber
  • 90 (xsd:string)