This paper proposes a variant of the well-known boosting trees algorithm to estimate conditional distributions.
In the present note, we present a general methodology to process text data using neural networks, and how it can be used to determine the severity of a cyber incident when this information is missing.
Hainaut et al. (2022) established that boosting can be conducted directly on the response under Tweedie loss function and log-link, by adapting the weights at each step. This is particularly useful to analyze low counts…
This article proposes a tail index partition-based rules extraction method that is able to construct estimates of the partition subsets and estimates of the tail index values.
This article proposes an alternative to standard pricing methods based on physics-inspired neural networks (PINNs)…
The present note aims to assess the impact of autocalibration on the goodness of lift.
In this note, an elementary approach is proposed, treating broker’s efficiency level as a fixed effect (rather than a random effect in credibility).
In this work, we consider a simple way to model accumulation episodes (i.e., large number of claims occurring in a short amount of time) in the context of cyber risk.
This article proposes an alternative to standard pricing methods based on physics-inspired neural networks (PINNs)…
This FAQctuary compares the behaviour of three measures on different datasets.
We adapt the Heath-Jarrow Morton (HJM) framework by considering a constraint of convergence of future forward rates toward a constant exogeneous rate set e.g. by a regulator.
This short note aims to extend their results to the Tweedie family of distributions, to which the Poisson law belongs.
