Joint Distributional Copula Regression for Insurance Claim Frequency and Severity via Boosting
In this paper, we propose a model-based boosting approach for fitting structured additive distributional copula regression models with bivariate responses, focusing particularly on the joint modeling of claim frequency and claim severity. The proposed approach is based on generalized additive models for location, scale, and shape (GAMLSS), which extend the general idea of GAMs by allowing multiple parameters of a response distribution, rather than only its conditional mean, to depend on covariates. The model is therefore intrinsically interpretable at the level of its additive predictors, providing greater transparency and auditability than black-box alternatives. We evaluate the proposed...
| Authors | Banghee So, Na Zhang |
|---|---|
| Primary category | stat.ML |
| Published | 27 October 2026 |
| Last updated at source | 27 October 2026 |
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