Large deviations of \(\ell^p\)-blocks of regularly varying time series and applications to cluster inference
DOI10.1016/j.spa.2023.03.013zbMath1524.60058arXiv2106.12822OpenAlexW4226276016MaRDI QIDQ6157001
Gloria Buriticá, Olivier Wintenberger, Thomas Mikosch
Publication date: 19 June 2023
Published in: Stochastic Processes and their Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2106.12822
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Stationary stochastic processes (60G10) Statistics of extreme values; tail inference (62G32) Large deviations (60F10)
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Cites Work
- Unnamed Item
- Adapting extreme value statistics to financial time series: dealing with bias and serial dependence
- A large deviations approach to limit theory for heavy-tailed time series
- Precise large deviations for dependent regularly varying sequences
- Regularly varying measures on metric spaces: hidden regular variation and hidden jumps
- Statistics for tail processes of Markov chains
- A functional limit theorem for dependent sequences with infinite variance stable limits
- Stable limits for sums of dependent infinite variance random variables
- Basic properties of strong mixing conditions. A survey and some open questions
- Limit theorems for empirical processes of cluster functionals
- Regularly varying multivariate time series
- Extremes and related properties of random sequences and processes
- Limit theory for moving averages of random variables with regularly varying tail probabilities
- Extremal index estimation for a weakly dependent stationary sequence
- An invariance principle for sums and record times of regularly varying stationary sequences
- The tail process revisited
- Tail measure and spectral tail process of regularly varying time series
- Inference on the tail process with application to financial time series modeling
- Polar decomposition of regularly varying time series in star-shaped metric spaces
- Point process and partial sum convergence for weakly dependent random variables with infinite variance
- Spectral tail processes and max-stable approximations of multivariate regularly varying time series
- Asymptotics for sliding blocks estimators of rare events
- Precise large deviations for dependent subexponential variables
- Estimation of cluster functionals for regularly varying time series: sliding blocks estimators
- Cluster based inference for extremes of time series
- The cluster index of regularly varying sequences with applications to limit theory for functions of multivariate Markov chains
- Regular variation for measures on metric spaces
- Extremes and local dependence in stationary sequences
- Heavy-Tailed Time Series
- Heavy-Tail Phenomena
- Large deviations of sums of independent random variables
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