3 papers
stat.ME2024
Parametric estimation of conditional Archimedean copula generators for censored data
Marie Michaelides, Hélène Cossette, Mathieu Pigeon
In this paper, we propose a novel approach for estimating Archimedean copula generators in a conditional setting, incorporating endogenous variables. Our method allows for the eval…
stat.ME2024
A non-parametric estimator for Archimedean copulas under flexible censoring scenarios and an application to claims reserving
Marie Michaelides, Hélène Cossette, Mathieu Pigeon
With insurers benefiting from ever-larger amounts of data of increasing complexity, we explore a data-driven method to model dependence within multilevel claims in this paper. More…
math.ST2022
Risk aggregation with FGM copulas
Christopher Blier-Wong, Hélène Cossette, Etienne Marceau
We offer a new perspective on risk aggregation with FGM copulas. Along the way, we discover new results and revisit existing ones, providing simpler formulas than one can find in t…