Extreme Risk Measures
Bayesian predictive quantile for generalized Pareto distribution
Bayesian predictive quantile for discrete generalized Pareto distribut...
Estimation of the scedasis function
Expectile Based Tail Index Estimation
High Expectile Estimation
Extreme Level Estimation
Multidimensional High Expectile Estimation
Estimation of generalized Pareto distributions
Expectile Computation
Marginal Expected Shortfall Expectile Based Estimation
Bayesian extreme quantile
Conditional Bayesian extreme quantile
Multidimensional Value-at-Risk (VaR) or Extreme Quantile (EQ) Estimati...
Value-at-Risk (VaR) or Extreme Quantile (EQ) Estimation
Maximum likelihood estimation of the parameters of the discrete genera...
Hill Tail Index Estimation
Wald-Type Hypothesis Testing
Maximum Likelihood Tail Index Estimation
Moment based Tail Index Estimation
Multidimensional Hill Tail Index Estimation
Plot empirical Bayes inference results for continuous and discrete gen...
Predictive posterior density of peak-over-threshold models
Conditional predictive posterior density of peaks-over-threshold model...
Extreme Expectile Estimation
Multidimensional Extreme Expectile Estimation
Predictive quantile based on the generalized Pareto model
Predictive quantile of peaks-over-threshold models
Marginal Expected Shortfall Quantile Based Estimation
Simulation of Two-Dimensional Temporally Dependent Observations
Simulation of -Dimensional Temporally Independent Observations
Simulation of One-Dimensional Temporally Dependent Observations
Test on the effect of concomitant covariate on the extremes of the res...
Test on the effect of concomitant covariate on the extremes of the res...
Test on tail homogeneity
A set of procedures for estimating risks related to extreme events via risk measures such as Expectile, Value-at-Risk, etc. is provided. Estimation methods for univariate independent observations and temporal dependent observations are available. The methodology is extended to the case of independent multidimensional observations. The statistical inference is performed through parametric and non-parametric estimators. Inferential procedures such as confidence intervals, confidence regions and hypothesis testing are obtained by exploiting the asymptotic theory. Adapts the methodologies derived in Padoan and Stupfler (2022) <doi:10.3150/21-BEJ1375>, Davison et al. (2023) <doi:10.1080/07350015.2022.2078332>, Daouia et al. (2018) <doi:10.1111/rssb.12254>, Drees (2000) <doi:10.1214/aoap/1019487617>, Drees (2003) <doi:10.3150/bj/1066223272>, de Haan and Ferreira (2006) <doi:10.1007/0-387-34471-3>, de Haan et al. (2016) <doi:10.1007/s00780-015-0287-6>, Padoan and Rizzelli (2024) <doi:10.3150/23-BEJ1668>, Daouia et al. (2024) <doi:10.3150/23-BEJ1632>.