Inference for the limiting cluster size distribution of extreme values
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Publication:1002158
DOI10.1214/07-AOS551zbMath1158.62061arXiv0810.1150MaRDI QIDQ1002158
Publication date: 25 February 2009
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/0810.1150
Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) Non-Markovian processes: estimation (62M09) Extreme value theory; extremal stochastic processes (60G70) Statistics of extreme values; tail inference (62G32) Inference from stochastic processes (62M99)
Related Items (20)
Compound Poisson approximation ⋮ Statistical analysis for stationary time series at extreme levels: new estimators for the limiting cluster size distribution ⋮ Cluster size distributions of extreme values for the Poisson-Voronoi tessellation ⋮ Asymptotic distributions for the intervals estimators of the extremal index and the cluster-size probabilities ⋮ Estimating the upcrossings index ⋮ Clusters of extremes: modeling and examples ⋮ Some variations on the extremal index ⋮ Method of moments estimators for the extremal index of a stationary time series ⋮ Regenerative block-bootstrap confidence intervals for tail and extremal indexes ⋮ A sliding blocks estimator for the extremal index ⋮ Estimation of extreme values by the average conditional exceedance rate method ⋮ Extreme M-quantiles as risk measures: from \(L^{1}\) to \(L^{p}\) optimization ⋮ Measures of serial extremal dependence and their estimation ⋮ On the measurement and treatment of extremes in time series ⋮ An efficient semiparametric maxima estimator of the extremal index ⋮ Weak convergence of a pseudo maximum likelihood estimator for the extremal index ⋮ Ordinal patterns in clusters of subsequent extremes of regularly varying time series ⋮ Estimating the multivariate extremal index function ⋮ Multiple thresholds in extremal parameter estimation ⋮ A horse race between the block maxima method and the peak-over-threshold approach
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