8 citations · 14 across the 4 of their papers we have counts for
4 papers
Stable network inference in high-dimensional graphical model using single-linkage
Emilie Devijver, Rémi Molinier, Mélina Gallopin
Stability, akin to reproducibility, is crucial in statistical analysis. This paper examines the stability of sparse network inference in high-dimensional graphical models, where se…
Prediction regions through Inverse Regression
Emilie Devijver, Emeline Perthame
Predict a new response from a covariate is a challenging task in regression, which raises new question since the era of high-dimensional data. In this paper, we are interested in t…
Nonlinear network-based quantitative trait prediction from transcriptomic data
Emilie Devijver, Mélina Gallopin, Emeline Perthame
Quantitatively predicting phenotype variables by the expression changes in a set of candidate genes is of great interest in molecular biology but it is also a challenging task for…
Block-diagonal covariance selection for high-dimensional Gaussian graphical models
Emilie Devijver, Mélina Gallopin
Gaussian graphical models are widely utilized to infer and visualize networks of dependencies between continuous variables. However, inferring the graph is difficult when the sampl…