loss_GPD_tensor: GPD tensor loss function for training a EQRN network

In EQRN: Extreme Quantile Regression Neural Networks for Risk Forecasting

GPD tensor loss function for training a EQRN network

Description

GPD tensor loss function for training a EQRN network

Usage

loss_GPD_tensor(
  out,
  y,
  orthogonal_gpd = TRUE,
  shape_penalty = 0,
  prior_shape = NULL,
  return_agg = c("mean", "sum", "vector", "nanmean", "nansum")
)

Arguments

out	Batch tensor of GPD parameters output by the network.
y	Batch tensor of corresponding response variable.
orthogonal_gpd	Whether the network is supposed to regress in the orthogonal reparametrization of the GPD parameters (recommended).
shape_penalty	Penalty parameter for the shape estimate, to potentially regularize its variation from the fixed prior estimate.
prior_shape	Prior estimate for the shape, used only if shape_penalty>0.
return_agg	The return aggregation of the computed loss over the batch. Must be one of ⁠"mean", "sum", "vector", "nanmean", "nansum"⁠.

Value

The GPD loss over the batch between the network output and the observed responses as a torch::Tensor, whose dimensions depend on return_agg.

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