# 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} }

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

#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 26 REFERENCES

A semiparametric Bayesian approach to extreme value estimation

- Mathematics, Computer Science
- 2012

- 42
- Open Access

An alternative to the Inverted Gamma for the variances to modelling outliers and structural breaks in dynamic models

- Mathematics
- 2014

- 13
- Open Access

A Dynamic Mixture Model for Unsupervised Tail Estimation without Threshold Selection

- Mathematics
- 2002

- 136
- Open Access

Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems

- Mathematics
- 1974

- 1,904
- Open Access