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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In the general signal+noise model we construct an empirical Bayes posteriorwhich we then use for uncertainty quantification for the unknown, possiblysparse, signal. We introduce a novel excessive bias restriction (EBR)condition, which gives rise to a new slicing of the entire space that issuitable for uncertainty quantification. Under EBR and some mild conditions onthe noise, we establish the local (oracle) optimality of the proposedconfidence ball. In passing, we also get the local optimal (oracle) results forestimation and posterior contraction problems. Adaptive minimax results (alsofor the estimation and posterior contraction problems) over various sparsityclasses follow from our local results.

Needles and straw in a haystack: robust confidence for possibly sparse sequences