dc.contributor.author | Tutz, Gerhard | de |
dc.contributor.author | Berger, Moritz | de |
dc.date.accessioned | 2024-03-14T13:06:26Z | |
dc.date.available | 2024-03-14T13:06:26Z | |
dc.date.issued | 2022 | de |
dc.identifier.issn | 1751-5823 | de |
dc.identifier.uri | https://www.ssoar.info/ssoar/handle/document/93052 | |
dc.description.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. | de |
dc.language | en | de |
dc.subject.ddc | Sozialwissenschaften, Soziologie | de |
dc.subject.ddc | Social sciences, sociology, anthropology | en |
dc.subject.other | adjacent categories model; cumulative model; dispersion; location-shift model; ordinal regression; proportional odds model; Vorwahl-Querschnitt (GLES 2013) (ZA5700 v2.0.0) | de |
dc.title | Sparser Ordinal Regression Models Based on Parametric and Additive Location-Shift Approaches | de |
dc.description.review | begutachtet (peer reviewed) | de |
dc.description.review | peer reviewed | en |
dc.source.journal | International Statistical Review | |
dc.source.volume | 90 | de |
dc.publisher.country | USA | de |
dc.source.issue | 2 | de |
dc.subject.classoz | Erhebungstechniken und Analysetechniken der Sozialwissenschaften | de |
dc.subject.classoz | Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods | en |
dc.subject.thesoz | Regression | de |
dc.subject.thesoz | regression | en |
dc.subject.thesoz | Modell | de |
dc.subject.thesoz | model | en |
dc.subject.thesoz | Statistik | de |
dc.subject.thesoz | statistics | en |
dc.identifier.urn | urn:nbn:de:0168-ssoar-93052-8 | |
dc.rights.licence | Creative Commons - Namensnennung, Nicht-kommerz. 4.0 | de |
dc.rights.licence | Creative Commons - Attribution-NonCommercial 4.0 | en |
ssoar.contributor.institution | FDB | de |
internal.status | formal und inhaltlich fertig erschlossen | de |
internal.identifier.thesoz | 10056459 | |
internal.identifier.thesoz | 10036422 | |
internal.identifier.thesoz | 10035432 | |
dc.type.stock | article | de |
dc.type.document | Zeitschriftenartikel | de |
dc.type.document | journal article | en |
dc.source.pageinfo | 306-327 | de |
internal.identifier.classoz | 10105 | |
internal.identifier.journal | 2129 | |
internal.identifier.document | 32 | |
internal.identifier.ddc | 300 | |
dc.identifier.doi | https://doi.org/10.1111/insr.12484 | de |
dc.description.pubstatus | Veröffentlichungsversion | de |
dc.description.pubstatus | Published Version | en |
internal.identifier.licence | 32 | |
internal.identifier.pubstatus | 1 | |
internal.identifier.review | 1 | |
internal.pdf.valid | false | |
internal.pdf.wellformed | true | |
internal.pdf.encrypted | false | |