Predict semi-conditional extreme quantiles using peaks over threshold

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:

• predictionsmatrix of dimension length(interm_quantiles_test) times length(prob_lvls_predict) containing the estimated extreme quantile at levels quantile, for each interm_quantiles_test,

• parsmatrix of dimension ntest times 2 containing the two GPD parameter MLEs, repeated length(interm_quantiles_test) times.