van der Wurp, HendrikGroll, Andreas2023-04-182023-04-182021-11-12http://hdl.handle.net/2003/4134110.17877/DE290R-23184In 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.enAStA advances in statistical analysis;Vol. 107. 2023, pp 127-151http://creativecommons.org/licenses/by/4.0/Count data regressionFIFA world cupsFootball penalisationJoint modellingRegularisation310article (journal)