Method of moments estimators for the extremal index of a stationary time series
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Publication:2199704
DOI10.1214/20-EJS1734zbMath1448.62131arXiv1912.08584MaRDI QIDQ2199704
Publication date: 14 September 2020
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1912.08584
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Order statistics; empirical distribution functions (62G30) Stationary stochastic processes (60G10)
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Statistical analysis for stationary time series at extreme levels: new estimators for the limiting cluster size distribution ⋮ Some variations on the extremal index ⋮ Statistics for heteroscedastic time series extremes
Cites Work
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- An efficient semiparametric maxima estimator of the extremal index
- Weak convergence of a pseudo maximum likelihood estimator for the extremal index
- Likelihood estimation of the extremal index
- Copula-based semiparametric models for multivariate time series
- Weak convergence for weighted empirical processes of dependent sequences
- Rank-based inference for bivariate extreme-value copulas
- Inference for the limiting cluster size distribution of extreme values
- Weak convergence of the tail empirical process for dependent sequences
- Extremes and related properties of random sequences and processes
- Extreme values for stationary and Markov sequences
- On the exceedance point process for a stationary sequence
- Extremal theory for stochastic processes
- Copulas and Markov processes
- Extremal index estimation for a weakly dependent stationary sequence
- Extremal behaviour of stationary Markov chains with applications
- Estimating the extremal index through local dependence
- Extremal behaviour of solutions to a stochastic difference equation with applications to ARCH processes
- Weighted approximations of tail processes for \(\beta\)-mixing random variables.
- Weak convergence and empirical processes. With applications to statistics
- A sliding blocks estimator for the extremal index
- GOODNESS-OF-FIT TESTS FOR MULTIVARIATE COPULA-BASED TIME SERIES MODELS
- Modelling pairwise dependence of maxima in space
- Asymptotic Statistics
- A nonparametric estimation procedure for bivariate extreme value copulas
- Inference for Clusters of Extreme Values
- Extremes and local dependence in stationary sequences
- Statistics of Extremes