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

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  • Hendrik van der Wurp

    (TU Dortmund University)

  • Andreas Groll

    (TU Dortmund University)

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.

Suggested Citation

  • Hendrik van der Wurp & Andreas Groll, 2023. "Introducing LASSO-type penalisation to generalised joint regression modelling for count data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 127-151, March.
    • Handle: RePEc:spr:alstar:v:107:y:2023:i:1:d:10.1007_s10182-021-00425-5
    DOI: 10.1007/s10182-021-00425-5
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    References listed on IDEAS

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