173 citations · 380 across the 27 of their papers we have counts for
19 papers · 1 filter
Separation rates for the detection of synchronization of interacting point processes in a mean field frame. Application to neuroscience
Josué Tchouanti, Éva Löcherbach, Patricia Reynaud-Bouret +1
Permutation tests have been proposed by Albert et al. (2015) to detect dependence between point processes, modeling in particular spike trains, that is the time occurrences of acti…
Non-asymptotic statistical test of the diffusion coefficient of stochastic differential equations
Anna Melnykova, Patricia Reynaud-Bouret, Adeline Samson
We develop several statistical tests of the determinant of the diffusion coefficient of a stochastic differential equation, based on discrete observations on a time interval $[0,T]…
Provable local learning rule by expert aggregation for a Hawkes network
Sophie Jaffard, Samuel Vaiter, Alexandre Muzy +1
We propose a simple network of Hawkes processes as a cognitive model capable of learning to classify objects. Our learning algorithm, named HAN for Hawkes Aggregation of Neurons, i…
Neural Coding as a Statistical Testing Problem
Guilherme Ost, Patricia Reynaud-Bouret
We take the testing perspective to understand what the minimal discrimination time between two stimuli is for different types of rate coding neurons. Our main goal is to describe t…
Sliding window strategy for convolutional spike sorting with Lasso : Algorithm, theoretical guarantees and complexity
Laurent Dragoni, Rémi Flamary, Karim Lounici +1
Spike sorting is a class of algorithms used in neuroscience to attribute the time occurences of particular electric signals, called action potential or spike, to neurons. We rephra…
Optimal Change-Point Detection and Localization
Nicolas Verzelen, Magalie Fromont, Matthieu Lerasle +1
Given a times series in , with a piece-wise contant mean and independent components, the twin problems of change-point detection and change-point localizati…
Exponential inequality for chaos based on sampling without replacement
P Hodara, Patricia Reynaud-Bouret
We are interested in the behavior of particular functionals, in a framework where the only source of randomness is a sampling without replacement. More precisely the aim of this sh…
Sparse space-time models: Concentration Inequalities and Lasso
Guilherme Ost, Patricia Reynaud-Bouret
Inspired by Kalikow-type decompositions, we introduce a new stochastic model of infinite neuronal networks, for which we establish sharp oracle inequalities for Lasso methods and r…
A data-dependent weighted LASSO under Poisson noise
Xin Jiang, Patricia Reynaud-Bouret, Vincent Rivoirard +2
Sparse linear inverse problems appear in a variety of settings, but often the noise contaminating observations cannot accurately be described as bounded by or arising from a Gaussi…
Bootstrap and permutation tests of independence for point processes
Mélisande Albert, Yann Bouret, Magalie Fromont +1
Motivated by a neuroscience question about synchrony detection in spike train analysis, we deal with the independence testing problem for point processes. We introduce non-parametr…
Lasso and probabilistic inequalities for multivariate point processes
Niels Richard Hansen, Patricia Reynaud-Bouret, Vincent Rivoirard
Due to its low computational cost, Lasso is an attractive regularization method for high-dimensional statistical settings. In this paper, we consider multivariate counting processe…
The two-sample problem for Poisson processes: adaptive tests with a non-asymptotic wild bootstrap approach
Magalie Fromont, Béatrice Laurent, Patricia Reynaud-Bouret
Considering two independent Poisson processes, we address the question of testing equality of their respective intensities. We first propose single tests whose test statistics are…
Nonparametric estimation of the division rate of a size-structured population
Marie Doumic Jauffret, Marc Hoffmann, Patricia Reynaud-Bouret +1
We consider the problem of estimating the division rate of a size-structured population in a nonparametric setting. The size of the system evolves according to a transport-fragment…
Adaptive density estimation: a curse of support?
Patricia Reynaud-Bouret, Vincent Rivoirard, Christine Tuleau-Malot
This paper deals with the classical problem of density estimation on the real line. Most of the existing papers devoted to minimax properties assume that the support of the underly…
Adaptive tests of homogeneity for a Poisson process
M. Fromont, B. Laurent, P. Reynaud-Bouret
We propose to test the homogeneity of a Poisson process observed on a finite interval. In this framework, we first provide lower bounds for the uniform separation rates in $\mathbb…
Calibration of thresholding rules for Poisson intensity estimation
Patricia Reynaud-Bouret, Vincent Rivoirard
In this paper, we deal with the problem of calibrating thresholding rules in the setting of Poisson intensity estimation. By using sharp concentration inequalities, oracle inequali…
Adaptive estimation for Hawkes processes; application to genome analysis
Patricia Reynaud-Bouret, Sophie Schbath
The aim of this paper is to provide a new method for the detection of either favored or avoided distances between genomic events along DNA sequences. These events are modeled by a…
Near optimal thresholding estimation of a Poisson intensity on the real line
Patricia Reynaud-Bouret, Vincent Rivoirard
The purpose of this paper is to estimate the intensity of a Poisson process by using thresholding rules. In this paper, the intensity, defined as the derivative of the mean mea…
Adaptive thresholding estimation of a Poisson intensity with infinite support
Patricia Reynaud-Bouret, Vincent Rivoirard
The purpose of this paper is to estimate the intensity of a Poisson process by using thresholding rules. In this paper, the intensity, defined as the derivative of the mean mea…