Principal Component Analysis for Multivariate Extremes
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Abstract (arXiv)
This chapter explores ways to reduce the dimensionality of the data while preserving key information relevant to the analysis of multivariate extreme values.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 34 references that are in the map. First posted on arXiv 5 Jun 2026.
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11 of 34 references.
References · 11
- 2016Decompositions of Dependence for High-Dimensional Extremescited by 13
- 2019k-means clustering of extremescited by 11
- 2015Sparsity in Multivariate Extremes with Applications to Anomaly Detectioncited by 9
- 2019Sparse regular variationcited by 8
- 2021Concentration bounds for the empirical angular measure with statistical learning applicationscited by 8
- 2016Sparse Representation of Multivariate Extremes with Applications to Anomaly Rankingcited by 5
- 2018Identifying groups of variables with the potential of being large simultaneouslycited by 5
- 2023Regular Variation in Hilbert Spaces and Principal Component Analysis for Functional Extremescited by 3
- 2024Information criteria for the number of directions of extremes in high-dimensional datacited by 2
- 2020Transformed-Linear Models for Time Series Extremescited by 1
- 2022Estimating probabilities of multivariate failure sets based on pairwise tail dependence coefficientscited by 1
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