UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Generalised Joint Regression for Count Data - A Penalty Extension for Competetive Settings

Marra, G; Kneib, T; Radice, R; Groll, A; Van der Wurp, H; (2020) Generalised Joint Regression for Count Data - A Penalty Extension for Competetive Settings. Statistics and Computing , 30 pp. 1419-1432. 10.1007/s11222-020-09953-7. Green open access

[thumbnail of Marra_Article_GeneralisedJointRegressionForC.pdf]
Preview
Text
Marra_Article_GeneralisedJointRegressionForC.pdf - Published Version

Download (799kB) | Preview

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.

Type: Article
Title: Generalised Joint Regression for Count Data - A Penalty Extension for Competetive Settings
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s11222-020-09953-7
Publisher version: https://doi.org/10.1007/s11222-020-09953-7
Language: English
Additional information: Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10101339
Downloads since deposit
14Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item