A Dynamic Mixture Model for Unsupervised Tail Estimation without Threshold Selection

@article{Frigessi2002ADM,
  title={A Dynamic Mixture Model for Unsupervised Tail Estimation without Threshold Selection},
  author={Arnoldo Frigessi and Ola Haug and H{\aa}vard Rue},
  journal={Extremes},
  year={2002},
  volume={5},
  pages={219-235}
}
  • Arnoldo Frigessi, Ola Haug, Håvard Rue
  • Published 2002
  • Mathematics
  • Exceedances over high thresholds are often modeled by fitting a generalized Pareto distribution (GPD) on R+. It is difficult to select the threshold, above which the GPD assumption is enough solid and enough data is available for inference. We suggest a new dynamically weighted mixture model, where one term of the mixture is the GPD, and the other is a light-tailed density distribution. The weight function varies on R+ in such a way that for large values the GPD component is predominant and… CONTINUE READING

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