Corpus ID: 235623745

Threshold selection for cluster inference based on large deviation principles

@inproceedings{Buritica2021ThresholdSF,
  title={Threshold selection for cluster inference based on large deviation principles},
  author={Gloria Buritic'a and T. Mikosch and O. Wintenberger},
  year={2021}
}
In the setting of regularly varying time series, a cluster of exceedances is a short period for which the supremum norm exceeds a high threshold. We propose to study a generalization of this notion considering short periods, or blocks, with `−norm above a high threshold. We derive large deviation principles of blocks and apply these results to improve cluster inference. We focus on blocks estimators and show they are consistent when we use large empirical quantiles from the `−norm of blocks as… Expand

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