English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Sparsity promoting norms are frequently used in high dimensional regression.A limitation of such Lasso-type estimators is that the optimal regularizationparameter depends on the unknown noise level. Estimators such as theconcomitant Lasso address this dependence by jointly estimating the noise leveland the regression coefficients. Additionally, in many applications, the datais obtained by averaging multiple measurements: this reduces the noisevariance, but it dramatically reduces sample sizes and prevents refined noisemodeling. In this work, we propose a concomitant estimator that can cope withcomplex noise structure by using non-averaged measurements. The resultingoptimization problem is convex and amenable, thanks to smoothing theory, tostate-of-the-art optimization techniques that leverage the sparsity of thesolutions. Practical benefits are demonstrated on toy datasets, realisticsimulated data and real neuroimaging data.

Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso