13 citations · 24 across the 18 of their papers we have counts for
18 papers
Conway--Maxwell multivariate Bernoulli distribution
Hélène Cossette, Etienne Marceau, Alessandro Mutti +1
We investigate the Conway--Maxwell multivariate Bernoulli distributions, a family of multivariate Bernoulli distributions derived from the Conway--Maxwell-binomial distribution. We…
Tree-structured Ising models under mean parameterization
Benjamin Côté, Hélène Cossette, Etienne Marceau
In the risk modeling literature, the Ising model has emerged as a valuable framework for dependent Bernoulli random variables, as its underlying graphical structure captures comple…
Extremal negative dependence and the strongly Rayleigh property
Hélène Cossette, Etienne Marceau, Alessandro Mutti +1
We provide a geometrical characterization of extremal negative dependence as a convex polytope in the simplex of multidimensional Bernoulli distributions, and we prove that it is a…
On a risk model with tree-structured Poisson Markov random field frequency, with application to rainfall events
Hélène Cossette, Benjamin Côté, Alexandre Dubeau +1
In many insurance contexts, dependence between risks of a portfolio may arise from their frequencies. We investigate a dependent risk model in which we assume the vector of count v…
Centrality and shape-related comparisons in a tree-structured Markov random field
Benjamin Côté, Hélène Cossette, Etienne Marceau
Understanding the effects of the choice of the tree on the joint distribution of a tree-structured Markov random field (MRF) is crucial for fully exploiting the intelligibility of…
Tree-structured Markov random fields with Poisson marginal distributions
Benjamin Côté, Hélène Cossette, Etienne Marceau
A new family of tree-structured Markov random fields for a vector of discrete counting random variables is introduced. According to the characteristics of the family, the marginal…
Generalized FGM dependence: Geometrical representation and convex bounds on sums
Hélène Cossette, Etienne Marceau, Alessandro Mutti +1
Building on the one-to-one relationship between generalized FGM copulas and multivariate Bernoulli distributions, we prove that the class of multivariate distributions with general…
Preventing RNN from Using Sequence Length as a Feature
Jean-Thomas Baillargeon, Hélène Cossette, Luc Lamontagne
Recurrent neural networks are deep learning topologies that can be trained to classify long documents. However, in our recent work, we found a critical problem with these cells: th…
A new method to construct high-dimensional copulas with Bernoulli and Coxian-2 distributions
Christopher Blier-Wong, Hélène Cossette, Sébastien Legros +1
We propose an approach to construct a new family of generalized Farlie-Gumbel-Morgenstern (GFGM) copulas that naturally scales to high dimensions. A GFGM copula can model moderate…
Collective risk models with FGM dependence
Christopher Blier-Wong, Hélène Cossette, Etienne Marceau
We study copula-based collective risk models when the dependence structure is defined by a Farlie-Gumbel-Morgenstern (FGM) copula. By leveraging a one-to-one correspondence between…
Individual Claims Reserving using Activation Patterns
Marie Michaelides, Mathieu Pigeon, Hélène Cossette
The occurrence of a claim often impacts not one but multiple insurance coverages provided in the contract. To account for this multivariate feature, we propose a new individual cla…
Efficient evaluation of risk allocations
Christopher Blier-Wong, Hélène Cossette, Etienne Marceau
Expectations of marginals conditional on the total risk of a portfolio are crucial in risk-sharing and allocation. However, computing these conditional expectations may be challeng…
Exchangeable FGM copulas
Christopher Blier-Wong, Hélène Cossette, Etienne Marceau
Copulas are a powerful tool to model dependence between the components of a random vector. One well-known class of copulas when working in two dimensions is the Farlie-GumbelMorgen…
Micro-level Reserving for General Insurance Claims using a Long Short-Term Memory Network
Ihsan Chaoubi, Camille Besse, Hélène Cossette +1
Detailed information about individual claims are completely ignored when insurance claims data are aggregated and structured in development triangles for loss reserving. In the hop…
Geographic ratemaking with spatial embeddings
Christopher Blier-Wong, Hélène Cossette, Luc Lamontagne +1
Spatial data is a rich source of information for actuarial applications: knowledge of a risk's location could improve an insurance company's ratemaking, reserving or risk managemen…
Rethinking Representations in P&C Actuarial Science with Deep Neural Networks
Christopher Blier-Wong, Jean-Thomas Baillargeon, Hélène Cossette +2
Insurance companies gather a growing variety of data for use in the insurance process, but most traditional ratemaking models are not designed to support them. In particular, many…
Tail approximations for sums of dependent regularly varying random variables under Archimedean copula models
Hélène Cossette, Etienne Marceau, Quang Huy Nguyen +1
In this paper, we compare two numerical methods for approximating the probability that the sum of dependent regularly varying random variables exceeds a high threshold under Archim…
Adjustment coefficient for risk processes in some dependent contexts
H. Cossette, E. Marceau, V. Maume-Deschamps
Following an article by Muller and Pflug, we study the adjustment coefficient of ruin theory in a context of temporal dependency. We provide a consistent estimator of this coeffici…