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 Regularisation |
URI: | http://hdl.handle.net/2003/41341 http://dx.doi.org/10.17877/DE290R-23184 |
Issue Date: | 2021-11-12 |
Rights link: | http://creativecommons.org/licenses/by/4.0/ |
Appears in Collections: | Statistical Methods for Big Data |
Files in This Item:
File | Description | Size | Format | |
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s10182-021-00425-5.pdf | 1.56 MB | Adobe PDF | View/Open |
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