Multivariate Generalized Linear Mixed Models for Joint Estimation of Sporting Outcomes
Applications
2019-03-01 v1
Abstract
This paper explores improvements in prediction accuracy and inference capability when allowing for potential correlation in team-level random effects across multiple game-level responses from different assumed distributions. First-order and fully exponential Laplace approximations are used to fit normal-binary and Poisson-binary multivariate generalized linear mixed models with non-nested random effects structures. We have built these models into the R package mvglmmRank, which is used to explore several seasons of American college football and basketball data.
Cite
@article{arxiv.1710.05284,
title = {Multivariate Generalized Linear Mixed Models for Joint Estimation of Sporting Outcomes},
author = {Jennifer E. Broatch and Andrew T. Karl},
journal= {arXiv preprint arXiv:1710.05284},
year = {2019}
}
Comments
To appear in the Italian Journal of Applied Statistics