RIVOIRARD , Vincent

The purpose of this paper is to estimate the intensity of a Poisson process $N$ by using thresholding rules. In this paper, the intensity, defined as the derivative of the mean measure of $N$ with respect to $ndx$ where $n$ is a fixed parameter, is assumed to be non-compactly supported. The estimator $\tilde{f}_{n,\gamma}$ based on random...

Due to its low computational cost, Lasso is an attractive regularization method for high-dimensional statistical settings. In this paper, we consider multivariate counting processes depending on an unknown function parameter to be estimated by linear combinations of a fixed dictionary. To select coefficients, we propose an adaptive...

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 underlying density is bounded and known. But this assumption may be very difficult to handle in practice. In this work, we show that, exactly as a curse of dimensionality...

We use Hawkes processes as models for spike trains analysis. A new Lasso method designed for general multivariate counting processes enables us to estimate the functional connectivity graph between the different recorded neurons.

We use Hawkes processes as models for spike trains analysis. A new Lasso method designed for general multivariate counting processes enables us to estimate the functional connectivity graph between the different recorded neurons.

En neurosciences, le principal objet d'étude est le train de spike car il est considéré comme le vecteur principal de transmission de l'information de l'activité cérébrale. Au fil des différentes études, plusieurs modélisations pour les trains de spikes ont été proposées, plus pour des raisons biologiques que mathématiques. Nous proposons ici...

En neurosciences, le principal objet d'étude est le train de spike car il est considéré comme le vecteur principal de transmission de l'information de l'activité cérébrale. Au fil des différentes études, plusieurs modélisations pour les trains de spikes ont été proposées, plus pour des raisons biologiques que mathématiques. Nous proposons ici...

When dealing with classical spike train analysis, the practitioner often per-forms goodness-of-fit tests to test whether the observed process is a Poisson process, for instance, or if it obeys another type of probabilistic model (Yana et al. in Bio-phys.. In doing so, there is a fundamental plug-in step, where the parameters of the supposed...

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-fragmentation equation: each individual grows with a given transport rate, and splits into two offsprings of the same size, following a binary fragmentation process with...

When dealing with classical spike train analysis, the practitioner often performs goodness-of-fit tests to test whether the observed process is a Poisson process, for instance, or if it obeys another type of probabilistic model. In doing so, there is a fundamental plug-in step, where the parameters of the supposed underlying model are...

When dealing with classical spike train analysis, the practitioner often performs goodness-of-fit tests to test whether the observed process is a Poisson process, for instance, or if it obeys another type of probabilistic model. In doing so, there is a fundamental plug-in step, where the parameters of the supposed underlying model are...

The distances between DNA Transcription Regulatory Elements (TRE) provide important clues to their dependencies and function within the gene regulation process. However, the locations of those TREs as well as their cross distances between occurrences are stochastic, in part due to the inherent limitations of Next Generation Sequencing methods...

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 Gaussian distribution. Poisson observations in particular are a characteristic feature of several real-world applications. Previous work on sparse Poisson inverse problems...

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 Gaussian distribution. Poisson observations in particular are a characteristic feature of several real-world applications. Previous work on sparse Poisson inverse problems...

International audience

Background: Statistical models that predict neuron spike occurrence from the earlier spiking activity of the whole recorded network are promising tools to reconstruct functional connectivity graphs. Some of the previously used methods were in the general statistical framework of the multivariate Hawkes processes but they often required huge...