Introducing LASSO-type penalisation to generalised joint regression modelling for count data

Abstract In this work, we propose an extension of the versatile joint regression framework for bivariate count responses package by Marra and Radice (R version 0.2-3, 2020) incorporating (adaptive) LASSO-type penalty. The underlying estimation algorithm is based on a quadratic approximation method enables variable selection corresponding estimates guarantee shrinkage sparsity. Hence, approach particularly useful in high-dimensional response settings. proposal’s empirical performance investigated simulation study application FIFA World Cup football data.