
Cluster based inference for extremes of time series
We introduce a new type of estimator for the spectral tail process of a ...
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Distribution sensitive estimators of the index of regular variation based on ratios of order statistics
Ratios of central order statistics seem to be very useful for estimating...
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VAR estimators using binary measurements
In this paper, two novel algorithms to estimate a Gaussian Vector Autore...
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Bias Reduced Peaks over Threshold Tail Estimation
In recent years several attempts have been made to extend tail modelling...
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On asymptotic behavior of the prediction error for a class of deterministic stationary sequences
One of the main problem in prediction theory of stationary processes X(t...
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Nonparametric estimation of marginal distributions for unordered pairs
In this article, we consider the estimation of the marginal distribution...
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Estmiation of the Spectral Measure from Convex Combinations of Regularly Varying Random Vectors
The extremal dependence structure of a regularly varying random vector X...
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PeakoverThreshold Estimators for Spectral Tail Processes: Random vs Deterministic Thresholds
The extreme value dependence of regularly varying stationary time series can be described by the spectral tail process. Drees et al. (2015) proposed estimators of the marginal distributions of this process based on exceedances over high deterministic thresholds and analyzed their asymptotic behavior. In practice, however, versions of the estimators are applied which use exceedances over random thresholds like intermediate order statistics. We prove that these modified estimators have the same limit distributions. This finding is corroborated in a simulation study, but the version using order statistics performs a bit better for finite samples.
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