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

Detecting an abrupt and persistent change in the underlying distribution ofonline data streams is an important problem in many applications. This paperproposes a new robust score-based algorithm called RSCUSUM, which can beapplied to unnormalized models and addresses the issue of unknown post-changedistributions. RSCUSUM replaces the Kullback-Leibler divergence with the Fisherdivergence between pre- and post-change distributions for computationalefficiency in unnormalized statistical models and introduces a notion of the``least favorable'' distribution for robust change detection. The algorithm andits theoretical analysis are demonstrated through simulation studies.

Robust Quickest Change Detection for Unnormalized Models