Corpus ID: 218869739

Estimation of cluster functionals for regularly varying time series: sliding blocks estimators

@article{Cissokho2020EstimationOC,
  title={Estimation of cluster functionals for regularly varying time series: sliding blocks estimators},
  author={Youssouph Cissokho and Rafal Kulik},
  journal={arXiv: Statistics Theory},
  year={2020}
}
Cluster indices describe extremal behaviour of stationary time series. We consider their sliding blocks estimators. Using a modern theory of multivariate, regularly varying time series, we obtain central limit theorems under conditions that can be easily verified for a large class of models. In particular, we show that in the Peak over Threshold framework, sliding and disjoint blocks estimators have the same limiting variance. 
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Statistical analysis for stationary time series at extreme levels: new estimators for the limiting cluster size distribution.
Threshold selection for cluster inference based on large deviation principles

References

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