predict_GPD_semiconditional: Predict semi-conditional extreme quantiles using peaks over...

In EQRN: Extreme Quantile Regression Neural Networks for Risk Forecasting

Predict semi-conditional extreme quantiles using peaks over threshold

Description

Predict semi-conditional extreme quantiles using peaks over threshold

Usage

predict_GPD_semiconditional(
  Y,
  interm_lvl,
  thresh_quantiles,
  interm_quantiles_test = thresh_quantiles,
  prob_lvls_predict = c(0.99)
)

Arguments

Y	Vector of ("training") observations.
interm_lvl	Probability level at which the empirical quantile should be used as the intermediate threshold.
thresh_quantiles	Numerical vector of the same length as Y representing the varying intermediate threshold on the train set.
interm_quantiles_test	Numerical vector of the same length as Y representing the varying intermediate threshold used for prediction on the test set.
prob_lvls_predict	Probability levels at which to predict the extreme semi-conditional quantiles.

Value

Named list containing:

predictions	matrix of dimension length(interm_quantiles_test) times length(prob_lvls_predict) containing the estimated extreme quantile at levels quantile, for each interm_quantiles_test,
pars	matrix of dimension ntest times 2 containing the two GPD parameter MLEs, repeated length(interm_quantiles_test) times.

EQRN documentation built on April 4, 2025, 12:45 a.m.