A Semiparametric Bayesian Extreme Value Model Using a Dirichlet Process Mixture of Gamma Densities

@article{Fquene2013ASB,
  title={A Semiparametric Bayesian Extreme Value Model Using a Dirichlet Process Mixture of Gamma Densities},
  author={Jairo F{\'u}quene},
  journal={arXiv: Machine Learning},
  year={2013}
}
  • Jairo Fúquene
  • Published 2013
  • Mathematics
  • arXiv: Machine Learning
  • In this paper we propose a model with a Dirichlet process mixture of gamma densities in the bulk part below threshold and a generalized Pareto density in the tail for extreme value estimation. The proposed model is simple and flexible allowing us posterior density estimation and posterior inference for high quantiles. The model works well even for small sample sizes and in the absence of prior information. We evaluate the performance of the proposed model through a simulation study. Finally… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 26 REFERENCES
    A default Bayesian procedure for the generalized Pareto distribution
    • 55
    A flexible extreme value mixture model
    • 97
    A semiparametric Bayesian approach to extreme value estimation
    • 42
    • Open Access
    Bayesian Density Estimation and Inference Using Mixtures
    • 2,122
    • Open Access
    Mixtures of Gamma Distributions With Applications
    • 114
    Bayesian analysis of extreme events with threshold estimation
    • 156
    • Open Access
    Estimating normal means with a conjugate style dirichlet process prior
    • 438
    A Dynamic Mixture Model for Unsupervised Tail Estimation without Threshold Selection
    • 136
    • Open Access
    Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems
    • 1,904
    • Open Access