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20092021
most citedDetection and localization of change-points in high-dimensional network traffic data

73 citations · 140 across the 10 of their papers we have counts for

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9 papers · 1 filter

math.ST2021

Sign Consistency of the Generalized Elastic Net Estimator

Wencan Zhu, Eric Adjakossa, Céline Lévy-Leduc +1

In this paper, we propose a novel variable selection approach in the framework of high-dimensional linear models where the columns of the design matrix are highly correlated. It co…

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…

math.ST2015

Improving heritability estimation by a variable selection approach in sparse high dimensional linear mixed models

Anna Bonnet, Céline Lévy-Leduc, Elisabeth Gassiat +2

Motivated by applications in neuroanatomy, we propose a novel methodology for estimating the heritability which corresponds to the proportion of phenotypic variance which can be ex…

math.ST2014★ 3 cited

Heritability estimation in high dimensional linear mixed models

Anna Bonnet, Elisabeth Gassiat, Céline Lévy-Leduc

Motivated by applications in genetic fields, we propose to estimate the heritability in high dimensional sparse linear mixed models. The heritability determines how the variance is…

math.ST2011★ 61 cited

Homogeneity and change-point detection tests for multivariate data using rank statistics

Alexandre Lung-Yut-Fong, Céline Lévy-Leduc, Olivier Cappé

Detecting and locating changes in highly multivariate data is a major concern in several current statistical applications. In this context, the first contribution of the paper is a…