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Generalised joint regression for count data: a penalty extension for competitive settings

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: a penalty extension for competitive settings. Statistics and Computing, doi: 10.1007/s11222-020-09953-7

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 competitive settings, 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 competitive settings. Finally, the method is applied to football data, showing its benefits compared to the standard approach with regard to predictive performance.

Publication Type: Article
Publisher Keywords: Count data regression, Joint modelling, Competitive Settings, Regularisation, Football
Subjects: G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography
G Geography. Anthropology. Recreation > GV Recreation Leisure
H Social Sciences > HA Statistics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Business School > Actuarial Science & Insurance
Date Deposited: 16 Jul 2020 11:56
URI: https://openaccess.city.ac.uk/id/eprint/24519
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