3 papers
stat.ME2020
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…
math.ST2019
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…
math.ST2016
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…