7 citations · 18 across the 21 of their papers we have counts for
23 papers
Spatiotemporally Consistent Multivariate Bias Correction for Climate Projections via Nested Vine Copulas
Theresa Meier, Erwan Koch, Valérie Chavez-Demoulin +1
Climate models are essential for understanding large-scale climate dynamics and long-term climate change, yet they exhibit systematic biases when compared with historical observati…
Investigating the Robustness of Extreme Precipitation Super-Resolution Across Climates
Louise Largeau, Tom Beucler, David Leutwyler +3
The coarse spatial resolution of gridded climate models, such as general circulation models, limits their direct use in projecting socially relevant variables like extreme precipit…
Statistical Inference on the Miss Distance Compared to Collision Probability for Conjunction Analysis
Soumaya Elkantassi, Valérie Chavez-Demoulin, Anthony C. Davison +2
Satellite conjunctions involving near misses of space objects are increasingly common, especially with the growth of satellite constellations and space debris. Accurate risk analys…
Causal Discovery in Multivariate Extremes with a Hydrological Analysis of Swiss River Discharges
Linda Mhalla, Valérie Chavez-Demoulin, Philippe Naveau
Causal asymmetry is based on the principle that an event is a cause only if its absence would not have been a cause. From there, uncovering causal effects becomes a matter of compa…
SEMF: Supervised Expectation-Maximization Framework for Predicting Intervals
Ilia Azizi, Marc-Olivier Boldi, Valérie Chavez-Demoulin
This work introduces the Supervised Expectation-Maximization Framework (SEMF), a versatile and model-agnostic approach for generating prediction intervals with any ML model. SEMF e…
Causality and extremes
Valérie Chavez-Demoulin, Linda Mhalla
In this work, we summarize the state-of-the-art methods in causal inference for extremes. In a non-exhaustive way, we start by describing an extremal approach to quantile treatment…
Structural restrictions in local causal discovery: identifying direct causes of a target variable
Juraj Bodik, Valérie Chavez-Demoulin
We consider the problem of learning a set of direct causes of a target variable from an observational joint distribution. Learning directed acyclic graphs (DAGs) that represent the…
Tail asymptotics and precise large deviations for some Poisson cluster processes
Fabien Baeriswyl, Valérie Chavez-Demoulin, Olivier Wintenberger
We study the tail asymptotics of two functionals (the maximum and the sum of the marks) of a generic cluster in two sub-models of the marked Poisson cluster process, namely the ren…
Identifiability of causal graphs under nonadditive conditionally parametric causal models
Juraj Bodik, Valérie Chavez-Demoulin
Existing approaches to causal discovery often rely on restrictive modeling assumptions that limit their applicability in real-world settings, particularly when data are heavy-taile…
Detecting causal covariates for extreme dependence structures
Juraj Bodik, Linda Mhalla, Valérie Chavez-Demoulin
Determining the causes of extreme events is a fundamental question in many scientific fields. An important aspect when modelling multivariate extremes is the tail dependence. In ap…
Linear prediction of point process times and marks
Maximilian Aigner, Valérie Chavez-Demoulin
In this paper, we are interested in linear prediction of a particular kind of stochastic process, namely a marked temporal point process. The observations are event times recorded…
Causal Modelling of Heavy-Tailed Variables and Confounders with Application to River Flow
Olivier C. Pasche, Valérie Chavez-Demoulin, Anthony C. Davison
Confounding variables are a recurrent challenge for causal discovery and inference. In many situations, complex causal mechanisms only manifest themselves in extreme events, or tak…
Climate-Related Disasters and the Death Toll
Valerie Chavez-Demoulin, Eric Jondeau, Linda Mhalla
With climate change accelerating, the frequency of climate disasters is expected to increase in the decades to come. There is ongoing debate as to how different climatic regions wi…
A competing risks interpretation of Hawkes processes
Maximilian Aigner, Valérie Chavez-Demoulin
We give a construction of the Hawkes process as a piecewise competing risks model. We argue that the most natural interpretation of the self-excitation kernel is the hazard functio…
Modelling the Extremes of Seasonal Viruses and Hospital Congestion: The Example of Flu in a Swiss Hospital
Setareh Ranjbar, Eva Cantoni, Valérie Chavez-Demoulin +3
Viruses causing flu or milder coronavirus colds are often referred to as "seasonal viruses" as they tend to subside in warmer months. In other words, meteorological conditions tend…
Intraday Retail Sales Forecast: An Efficient Algorithm for Quantile Additive Modeling
Marc-Olivier Boldi, Valérie Chavez-Demoulin, Olivier Gallay
With the ever increasing prominence of data in retail operations, sales forecasting has become an essential pillar in the efficient management of inventories. When facing high dema…
Analyzing privacy-aware mobility behavior using the evolution of spatio-temporal entropy
Arielle Moro, Benoît Garbinato, Valérie Chavez-Demoulin
Analyzing mobility behavior of users is extremely useful to create or improve existing services. Several research works have been done in order to study mobility behavior of users…
Causal mechanism of extreme river discharges in the upper Danube basin network
Linda Mhalla, Valérie Chavez-Demoulin, Debbie J. Dupuis
Extreme hydrological events in the Danube river basin may severely impact human populations, aquatic organisms, and economic activity. One often characterizes the joint structure o…
Discovering demographic data of users from the evolution of their spatio-temporal entropy
Arielle Moro, Benoît Garbinato, Valérie Chavez-Demoulin
Inferring information related to users enables to highly improve the quality of many mobile services. For example, knowing the demographic characteristics of a user allows a servic…
Exceedance-based nonlinear regression of tail dependence
Linda Mhalla, Thomas Opitz, Valérie Chavez-Demoulin
The probability and structure of co-occurrences of extreme values in multivariate data may critically depend on auxiliary information provided by covariates. In this contribution,…
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery
Natasa Tagasovska, Valérie Chavez-Demoulin, Thibault Vatter
Causal inference using observational data is challenging, especially in the bivariate case. Through the minimum description length principle, we link the postulate of independence…
Risk measure estimation for -mixing time series and applications
Valérie Chavez-Demoulin, Armelle Guillou
In this paper, we discuss the application of extreme value theory in the context of stationary -mixing sequences that belong to the Fréchet domain of attraction. In particular,…
Improving the local scoring algorithm using gradient sampling
Marc-Olivier Boldi, Valérie Chavez-Demoulin
We adapt the gradient sampling algorithm to the local scoring algorithm to solve complex estimation problems based on an optimization of an objective function. This overcomes non-d…