Many algorithms for high-dimensional regression problems require the calibration of
regularization hyperparameters. This, in turn, often requires the knowledge of the unknown …

A limitation of Lasso-type estimators is that the optimal regularization parameter depends on
the unknown noise level. Estimators such as the concomitant Lasso address this …

Noise covariance estimation in multi-task high-dimensional linear models Page 1 Bernoulli
30(3), 2024, 1695–1722 https://doi.org/10.3150/23-BEJ1644 Noise covariance estimation in …

In this paper, we develop an epsilon admissible subsets (EAS) model selection approach for
performing group variable selection in the high-dimensional multivariate regression setting …

We consider the problem of testing for the presence of linear relationships between large
sets of random variables based on a postselection inference approach to canonical …

Repeated measurements are common in many fields, where random variables are observed
repeatedly across different subjects. Such data have an underlying hierarchical structure …

Due to non-invasiveness and excellent time resolution, magneto-and
electroencephalography (M/EEG) have emerged as tools of choice to monitor brain activity …

Support recovery and sup-norm convergence rates for sparse pivotal estimation Page 1
Support recovery and sup-norm convergence rates for sparse pivotal estimation Quentin …

Understanding the functioning of the brain under normal and pathological conditions is one
of the challenges of the 21st century. In the last decades, neuroimaging has radically …