8 citations · 8 across the 7 of their papers we have counts for
11 papers
A Structured Estimator for large Covariance Matrices in the Presence of Pairwise and Spatial Covariates
Martin Metodiev, Marie Perrot-Dockès, Sarah Ouadah +4
We consider the problem of estimating a high-dimensional covariance matrix from a small number of observations when covariates on pairs of variables are available and the variables…
Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator
Martin Metodiev, Marie Perrot-Dockès, Sarah Ouadah +2
We propose an easily computed estimator of marginal likelihoods from posterior simulation output, via reciprocal importance sampling, combining earlier proposals of DiCiccio et al…
Sign-consistent estimation in a sparse Poisson model
Marina Gomtsyan, Céline Lévy-Leduc, Sarah Ouadah +1
In this work, we consider an estimation method in sparse Poisson models inspired by [1] and provide novel sign consistency results under mild conditions.
Variable selection in sparse multivariate GLARMA models: Application to germination control by environment
M. Gomtsyan, C. Lévy-Leduc, S. Ouadah +3
We propose a novel and efficient iterative two-stage variable selection approach for multivariate sparse GLARMA models, which can be used for modelling multivariate discrete-valued…
Variable selection in sparse GLARMA models
Marina Gomtsyan, Céline Lévy-Leduc, Sarah Ouadah +2
In this paper, we propose a novel and efficient two-stage variable selection approach for sparse GLARMA models, which are pervasive for modeling discrete-valued time series. Our ap…
Motif-based tests for bipartite networks
Sarah Ouadah, Pierre Latouche, Stéphane Robin
Bipartite networks are a natural representation of the interactions between entities from two different types. The organization (or topology) of such networks gives insight to unde…
Variable selection in sparse GLARMA models
M. Gomtsyan, C. Lévy-Leduc, S. Ouadah +1
In this paper, we propose a novel and efficient two-stage variable selection approach for sparse GLARMA models, which are pervasive for modeling discrete-valued time series. Our ap…
Variable selection in sparse high-dimensional GLARMA models
Céline Lévy-Leduc, Sarah Ouadah, Laure Sansonnet
In this paper, we propose a novel variable selection approach in the framework of sparse high-dimensional GLARMA models. It consists in combining the estimation of the autoregressi…
Nonparametric homogeneity tests and multiple change-point estimation for analyzing large Hi-C data matrices
Vincent Brault, Sarah Ouadah, Laure Sansonnet +1
We propose a novel nonparametric approach for estimating the location of block boundaries (change-points) of non-overlapping blocks in a random symmetric matrix which consists of r…
Goodness of fit of logistic models for random graphs
Pierre Latouche, Stéphane Robin, Sarah Ouadah
Logistic regression is a natural and simple tool to understand how covariates contribute to explain the topology of a binary network. Once the model fitted, the practitioner is int…
Degree-based goodness-of-fit tests for heterogeneous random graph models : independent and exchangeable cases
Sarah Ouadah, Stéphane Robin, Pierre Latouche
The degrees are a classical and relevant way to study the topology of a network. They can be used to assess the goodness-of-fit for a given random graph model. In this paper we int…