collaborators

6 papers

stat.ME2026

Hawkes Processes with Variable Length Memory: Existence, Inference and Application to Neuronal Activity

Sacha Quayle, Anna Bonnet, Maxime Sangnier

Multivariate Hawkes processes are past-dependant point processes originally introduced to model excitation effects, later extended to a nonlinear framework to account for the oppos…

math.ST2025

Hawkes process with a diffusion-driven baseline: long-run behavior, inference, statistical tests

Maya Sadeler Perrin, Anna Bonnet, Charlotte Dion-Blanc +1

Event-driven systems in fields such as neuroscience, social networks, and finance often exhibit dynamics influenced by continuously evolving external covariates. Motivated by these…

stat.ME2025

Spectral analysis for the inference of noisy Hawkes processes

Anna Bonnet, Felix Cheysson, Miguel Martinez Herrera +1

Classic estimation methods for Hawkes processes rely on the assumption that observed event times are indeed a realisation of a Hawkes process, without considering any potential per…

stat.ME2025

A Markov switching discrete-time Hawkes process: application to the monitoring of bats behavior

Anna Bonnet, Stéphane Robin

Over the past few decades, the Hawkes process has become a popular framework for modeling temporal events thanks to its flexibility to capture different dependency structures. The…

stat.ME2025

Testing procedures based on maximum likelihood estimation for Marked Hawkes processes

Anna Bonnet, Charlotte Dion-Blanc, Maya Sadeler-Perrin

The Hawkes model is a past-dependent point process, widely used in various fields for modeling temporal clustering of events. Extending this framework, the multidimensional marked…

stat.ML2025

Nonparametric estimation of Hawkes processes with RKHSs

Anna Bonnet, Maxime Sangnier

This paper addresses nonparametric estimation of nonlinear multivariate Hawkes processes, where the interaction functions are assumed to lie in a reproducing kernel Hilbert space (…