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

The problem of sequential change diagnosis is considered, where observationsare obtained on-line, an abrupt change occurs in their distribution, and thegoal is to quickly detect the change and accurately identify the post-changedistribution, while controlling the false alarm rate. A finite set ofalternatives is postulated for the post-change regime, but no prior informationis assumed for the unknown change-point. A drawback of many algorithms thathave been proposed for this problem is the implicit use of pre-change data fordetermining the post-change distribution. This can lead to very largeconditional probabilities of misidentification, given that there was no falsealarm, unless the change occurs soon after monitoring begins. A novel,recursive algorithm is proposed and shown to resolve this issue without the useof additional tuning parameters and without sacrificing control of theworst-case delay in Lorden's sense. A theoretical analysis is conducted for ageneral family of sequential change diagnosis procedures, which supports theproposed algorithm and revises certain state-of-the-art results. Additionally,a novel, comprehensive method is proposed for the design and evaluation ofsequential change diagnosis algorithms. This method is illustrated withsimulation studies, where existing procedures are compared to the proposed.

Sequential Change Diagnosis Revisited and the Adaptive Matrix CuSum