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

Hendrik van der Wurp () and Andreas Groll
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Hendrik van der Wurp: TU Dortmund University
Andreas Groll: TU Dortmund University

AStA Advances in Statistical Analysis, 2023, vol. 107, issue 1, No 7, 127-151

Abstract: 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.

Keywords: Count data regression; FIFA world cups; Football penalisation; Joint modelling; Regularisation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10182-021-00425-5

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