loss_fn_gmwmx_no_missing: Loss function for GMWMX without missing value

In gmwmx2: Estimate Functional and Stochastic Parameters of Linear Models with Correlated Residuals and Missing Data

Loss function for GMWMX without missing value

Description

Computes the weighted squared error between empirical wavelet variance and theoretical wavelet variance implied by a model and parameter vector.

Usage

loss_fn_gmwmx_no_missing(
  theta,
  model,
  n,
  prep,
  wv_obj,
  quantities_D,
  omega = NULL
)

Arguments

theta	Real-valued parameter vector.
model	A time_series_model or sum_model.
n	Length of autocovariance to compute.
prep	Output from prepare_optim_layout.
wv_obj	A wv::wvar object.
omega	Optional weighting matrix. If NULL, uses inverse CI width.

Value

Scalar objective value.

gmwmx2 documentation built on June 10, 2026, 5:06 p.m.