| Texto completo | |
| Autor(es): |
Graziadei, Helton
;
Marques F, Paulo C.
;
De Melo, Eduardo F. L.
;
Targino, Rodrigo S.
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | Journal of Applied Statistics; v. N/A, p. 20-pg., 2025-10-22. |
| Resumo | |
We present a model-agnostic framework for the construction of prediction intervals of insurance claims, with finite sample statistical guarantees, extending the technique of split conformal prediction to the domain of two-stage frequency-severity modeling. The framework effectiveness is showcased with simulated and real datasets using classical parametric models and contemporary machine learning methods. When the underlying severity model is a random forest, we extend the two-stage split conformal prediction algorithm, showing how the out-of-bag mechanism can be leveraged to eliminate the need for a calibration set in the conformal procedure. (AU) | |
| Processo FAPESP: | 23/02538-0 - Séries temporais, ondaletas, dados de alta dimensão e aplicações |
| Beneficiário: | Aluísio de Souza Pinheiro |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |