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 this note we consider spectral cut-off estimators to solve a statisticallinear inverse problem under arbitrary white noise. The truncation level isdetermined with a recently introduced adaptive method based on the classicaldiscrepancy principle. We provide probabilistic oracle inequalities togetherwith quantification of uncertainty for general linear problems. Moreover, wecompare the new method to existing ones, namely early stopping sequentialdiscrepancy principle and the balancing principle, both theoretically andnumerically.

A Probabilistic Oracle Inequality and Quantification of Uncertainty of a modified Discrepancy Principle for Statistical Inverse Problems