28 citations · 38 across the 9 of their papers we have counts for
9 papers
Multiple testing with the horseshoe
Sayantan Banerjee, Ismaël Castillo, Fanny Villers
We study multiple testing under continuous global--local shrinkage priors, with a focus on the horseshoe prior in high-dimensional sparse settings. While such priors provide adapti…
A Bayesian framework with adaptive elastic nets for the inference of Gaussian graphical models
Roland B. Sogan, Tabea Rebafka, Fanny Villers
Estimating conditional independence graphs from high-dimensional Gaussian data is challenging because methods must detect relevant edges while rigorously controlling statistical er…
Enhancing the Power of Gaussian Graphical Model Inference by Modeling the Graph Structure
Valentin Kilian, Tabea Rebafka, Fanny Villers
For the problem of inferring a Gaussian graphical model (GGM), this work explores the application of a recent approach from the multiple testing literature for graph inference. The…
Graph inference with clustering and false discovery rate control
Tabea Rebafka, Etienne Roquain, Fanny Villers
In this paper, a noisy version of the stochastic block model (NSBM) is introduced and we investigate the three following statistical inferences in this model: estimation of the mod…
A semiparametric extension of the stochastic block model for longitudinal networks
Catherine Matias, Tabea Rebafka, Fanny Villers
To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions b…
A Global Homogeneity Test for High-Dimensional Linear Regression
Camille Charbonnier, Nicolas Verzelen, Fanny Villers
This paper is motivated by the comparison of genetic networks based on microarray samples. The aim is to test whether the differences observed between two inferred Gaussian graphic…