Introducing LASSO-type penalisation to generalised joint regression modelling for count data - Hendrik van der Wurp; Andreas Groll (AStA Advances in Statistical Analysis, 2021)
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.
Keywords: 510; Count data regression; FIFA world cups; Football penalisation; Joint modelling; Regularisation