Document type
ArticleVersion
Published versionPublication date
Publication license
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/191293
Copula-based bivariate finite mixture regression models with an application for insurance claim count data
Journal Title
Authors
Director/Tutor
Journal ISSN
Volume Title
Related resource
Abstract
Modeling bivariate (or multivariate) count data has received increased interest in recent years. The aim is to model the number of different but correlated counts taking into account covariate information. Bivariate Poisson regression models based on the shock model approach are widely used because of their simple form and interpretation. However, these models do not allow for overdispersion or negative correlation, and thus, other models have been proposed in the literature to avoid these limitations. The present paper proposes copula-based bivariate finite mixture of regression models. These models offer some advantages since they have all the benefits of a finite mixture, allowing for unobserved heterogeneity and clustering effects, while the copula-based derivation can produce more flexible structures, including negative correlations and regressors. In this paper, the new approach is defined, estimation through an EM algorithm is presented, and then different models are applied to a Spanish insurance claim count database
Subject (English)
Citation
Citation
BERMÚDEZ, Lluís and KARLIS, Dimitris. Copula-based bivariate finite mixture regression models with an application for insurance claim count data. TEST. 2022. Vol. 31, num. 1082-1099. ISSN 1133-0686. [consulted: 11 of June of 2026]. Available at: https://hdl.handle.net/2445/191293