Authors: van der Wurp, Hendrik
Groll, Andreas
Title: Introducing LASSO-type penalisation to generalised joint regression modelling for count data
Language (ISO): en
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.
Subject Headings: Count data regression
FIFA world cups
Football penalisation
Joint modelling
Issue Date: 2021-11-12
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Appears in Collections:Statistical Methods for Big Data

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