Introducing LASSO-type penalisation to generalised joint regression modelling for count data
| dc.contributor.author | van der Wurp, Hendrik | |
| dc.contributor.author | Groll, Andreas | |
| dc.date.accessioned | 2023-04-18T08:55:16Z | |
| dc.date.available | 2023-04-18T08:55:16Z | |
| dc.date.issued | 2021-11-12 | |
| dc.description.abstract | In this work, we propose an extension of the versatile joint regression framework for bivariate count responses of the R package GJRM by Marra and Radice (R package version 0.2-3, 2020) by incorporating an (adaptive) LASSO-type penalty. The underlying estimation algorithm is based on a quadratic approximation of the penalty. The method enables variable selection and the corresponding estimates guarantee shrinkage and sparsity. Hence, this approach is particularly useful in high-dimensional count response settings. The proposal’s empirical performance is investigated in a simulation study and an application on FIFA World Cup football data. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/41341 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-23184 | |
| dc.language.iso | en | de |
| dc.relation.ispartofseries | AStA advances in statistical analysis;Vol. 107. 2023, pp 127-151 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Count data regression | en |
| dc.subject | FIFA world cups | en |
| dc.subject | Football penalisation | en |
| dc.subject | Joint modelling | en |
| dc.subject | Regularisation | en |
| dc.subject.ddc | 310 | |
| dc.title | Introducing LASSO-type penalisation to generalised joint regression modelling for count data | en |
| dc.type | Text | de |
| dc.type.publicationtype | article | de |
| dcterms.accessRights | open access | |
| eldorado.secondarypublication | true | de |
| eldorado.secondarypublication.primarycitation | AStA advances in statistical analysis. Vol.107. 2023, pp 127–151 | en |
| eldorado.secondarypublication.primaryidentifier | https://doi.org/10.1007/s10182-021-00425-5 | de |