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

In threshold-based anomaly detection, we want to tune the threshold of adetector to achieve an acceptable false alarm rate. However, tuning thethreshold is often a non-trivial task due to unknown detector outputdistributions. A detector threshold that provides an acceptable false alarmrate is equivalent to a specific quantile of the detector output distribution.Therefore, we use quantile estimators based on order statistics to estimate thedetector threshold. The estimation of quantiles from sample data has a morethan a century long tradition and we provide three different distribution-freefinite sample guarantees for a class of quantile estimators. The first is basedon the Dworetzky-Kiefer-Wolfowitz inequality, the second utilizes theVysochanskij-Petunin inequality, and the third is based on exact confidenceintervals for a beta distribution. These guarantees are then compared and usedin the detector threshold tuning problem. We use both simulated data as well asdata obtained from an experimental setup with the Temperature Control Lab tovalidate the guarantees provided.

Finite sample guarantees for quantile estimation: An application to detector threshold tuning