Estimation and uncertainty quantification for extreme quantile regions

@article{Branger2019EstimationAU,
  title={Estimation and uncertainty quantification for extreme quantile regions},
  author={B. B{\'e}ranger and Simone A. Padoan and Scott Anthony Sisson},
  journal={Extremes},
  year={2019},
  volume={24},
  pages={349-375}
}
Estimation of extreme quantile regions, spaces in which future extreme events can occur with a given low probability, even beyond the range of the observed data, is an important task in the analysis of extremes. Existing methods to estimate such regions are available, but do not provide any measures of estimation uncertainty. We develop univariate and bivariate schemes for estimating extreme quantile regions under the Bayesian paradigm that outperforms existing approaches and provides natural… 
Reference Priors for the Generalized Extreme Value Distribution
  • Likun Zhang, B. Shaby
  • Statistica Sinica
  • 2022
We derive a collection of reference prior distributions for Bayesian analysis under the three-parameter generalized extreme value (GEV) distribution. These priors are based on an established formal
Estimating Concurrent Climate Extremes: A Conditional Approach
TLDR
This work develops a statistical framework for estimating bivariate concurrent extremes via a conditional approach, where univariate extreme value modeling is combined with dependence modeling of the conditional tail distribution using techniques from quantile regression and extreme value analysis to quantify concurrent extremes.

References

SHOWING 1-10 OF 53 REFERENCES
Estimating extreme bivariate quantile regions
When simultaneously monitoring two possibly dependent, positive risks one is often interested in quantile regions with very small probability p. These extreme quantile regions contain hardly any or
A semi‐parametric stochastic generator for bivariate extreme events
The analysis of multiple extreme values aims to describe the stochastic behaviour of observations in the joint upper tail of a distribution function. For instance, being able to simulate multivariate
Estimation of extreme risk regions under multivariate regular variation
When considering d possibly dependent random variables, one is often interested in extreme risk regions, with very small probability p . We consider risk regions of the form { z ∈ R d : f ( z ) ≤ β
Likelihood estimators for multivariate extremes
The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter
Forecast verification for extreme value distributions with an application to probabilistic peak wind prediction
Predictions of the uncertainty associated with extreme events are a vital component of any prediction system for such events. Consequently, the prediction system ought to be probabilistic in nature,
A nonparametric method for producing isolines of bivariate exceedance probabilities
We present a method for drawing isolines indicating regions of equal joint exceedance probability for bivariate data. The method relies on bivariate regular variation, a dependence framework widely
Probabilistic wind power forecasts using local quantile regression
TLDR
It can be shown that, for some purposes, forecasts in terms of quantiles provide the type of information required to make optimal economic decisions.
Probabilistic Forecasts of Precipitation in Terms of Quantiles Using NWP Model Output
Abstract At sites with observations it is often possible to improve or enrich NWP model forecasts by means of statistical methods. Such forecasts are almost exclusively deterministic or probabilities
Statistics for near independence in multivariate extreme values
We propose a multivariate extreme value threshold model for joint tail estimation which overcomes the problems encountered with existing techniques when the variables are near independence. We
Estimation of Extreme Conditional Quantiles
21 The estimation of extreme quantiles of the response distribution is of great interest 22 in many areas. Extreme value theory provides a useful tool for estimating extreme 23 quantiles. However,
...
1
2
3
4
5
...