Extreme value theory for anomaly detection -- the GPD classifier
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Publication:2027086
DOI10.1007/S10687-020-00393-0zbMath1469.62256OpenAlexW3086241558MaRDI QIDQ2027086
Sebastian Engelke, Edoardo Vignotto
Publication date: 21 May 2021
Published in: Extremes (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10687-020-00393-0
clusteringmachine learningstatistical methodsgeneralized Pareto distributionnovelty detectionopen set classification
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Statistics of extreme values; tail inference (62G32) Learning and adaptive systems in artificial intelligence (68T05)
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Cites Work
- Estimation of extreme risk regions under multivariate regular variation
- Bridging centrality and extremity: refining empirical data depth using extreme value statistics
- On estimating the endpoint of a distribution
- A simple general approach to inference about the tail of a distribution
- Sparse representation of multivariate extremes with applications to anomaly detection
- An optimal algorithm for approximate nearest neighbor searching fixed dimensions
- Estimation of Parameters and Larger Quantiles Based on the k Largest Observations
- An Introduction to Statistical Learning
- Estimation of Extreme Depth-Based Quantile Regions
- An introduction to statistical modeling of extreme values
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