Artikel
Introducing LASSO-type penalisation to generalised joint regression modelling for count data
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.
- Sprache
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Englisch
- Erschienen in
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Journal: AStA Advances in Statistical Analysis ; ISSN: 1863-818X ; Volume: 107 ; Year: 2021 ; Issue: 1-2 ; Pages: 127-151 ; Berlin, Heidelberg: Springer
- Klassifikation
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Mathematik
- Thema
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Count data regression
FIFA world cups
Football penalisation
Joint modelling
Regularisation
- Ereignis
-
Geistige Schöpfung
- (wer)
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van der Wurp, Hendrik
Groll, Andreas
- Ereignis
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Veröffentlichung
- (wer)
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Springer
- (wo)
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Berlin, Heidelberg
- (wann)
-
2021
- DOI
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doi:10.1007/s10182-021-00425-5
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
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Objekttyp
- Artikel
Beteiligte
- van der Wurp, Hendrik
- Groll, Andreas
- Springer
Entstanden
- 2021
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