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Generalised joint regression for count data with a focus on modelling football matches

van der Wurp, H., Groll, A., Kneib, T., Marra, G. and Radice, R. ORCID: 0000-0002-6316-3961 (2020). Generalised joint regression for count data with a focus on modelling football matches. Statistics and Computing,

Abstract

We propose a versatile joint regression framework for count responses. The method is implemented in the R add-on package GJRM and allows for modelling linear and non-linear dependence through the use of several copulae. Moreover, the parameters of the marginal distributions of the count responses and of the copula can be specified as flexible functions of covariates. Motivated by a football application, we also discuss an extension which forces the regression coefficients of the marginal (linear) predictors to be equal via a suitable penalisation. Model fitting is based on a trust region algorithm which estimates simultaneously all the parameters of the joint models. We investigate the proposal’s empirical performance in two simulation studies, the first one designed for arbitrary count data, the other one reflecting football-specific settings. Finally, the method is applied to FIFA World Cup data, showing its competitiveness to the standard approach with regard to predictive performance.

Publication Type: Article
Additional Information: This is a post-peer-review, pre-copyedit version of an article to be published in Statistics and Computing. The final authenticated version will be available online at: http://link.springer.com/journal/11222
Publisher Keywords: Count data regression, FIFA World Cups, Football, Joint modelling, Regularization
Subjects: G Geography. Anthropology. Recreation > GV Recreation Leisure
H Social Sciences > HA Statistics
H Social Sciences > HD Industries. Land use. Labor
Departments: Business School > Actuarial Science & Insurance
Date Deposited: 22 Jun 2020 11:15
URI: https://openaccess.city.ac.uk/id/eprint/24369
[img] Text - Accepted Version
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