Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/286862 
Year of Publication: 
2021
Citation: 
[Journal:] AStA Advances in Statistical Analysis [ISSN:] 1863-818X [Volume:] 107 [Issue:] 1-2 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2021 [Pages:] 127-151
Publisher: 
Springer, Berlin, Heidelberg
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.
Subjects: 
Count data regression
FIFA world cups
Football penalisation
Joint modelling
Regularisation
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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