loss_GPD: Generalized Pareto likelihood loss

In EQRN: Extreme Quantile Regression Neural Networks for Risk Forecasting

Generalized Pareto likelihood loss

Description

Generalized Pareto likelihood loss

Usage

loss_GPD(
  sigma,
  xi,
  y,
  rescaled = TRUE,
  interm_lvl = NULL,
  return_vector = FALSE
)

Arguments

sigma	Value(s) for the GPD scale parameter.
xi	Value(s) for the GPD shape parameter.
y	Vector of observations
rescaled	Whether y already is a vector of excesses (TRUE) or needs rescaling (FALSE).
interm_lvl	Probability level at which the empirical quantile should be used as the intermediate threshold to compute the excesses, if rescaled==FALSE.
return_vector	Whether to return the the vector of GPD losses for each observation instead of the negative log-likelihood (average loss).

Value

GPD negative log-likelihood of the GPD parameters over the sample of observations.

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