| emtp2 {graphicalExtremes} | R Documentation |
Performs Gaussian likelihood optimization under Laplacian matrix constraints.
Description
This function implements a block descent algorithm to find the maximum of the Gaussian log-likelihood under the constraint that the concentration matrix is a Laplacian matrix. See Röttger et al. (2021) for details.
Usage
emtp2(Gamma, tol = 1e-06, verbose = TRUE, initial_point = TRUE)
Arguments
Gamma |
conditionally negative semidefinite matrix. This will be typically the empirical variogram matrix. |
tol |
The convergence tolerance. The algorithm terminates when the sum of absolute differences between two iterations is below |
verbose |
if TRUE (default) the output will be printed. |
initial_point |
if TRUE (default), the algorithm will construct an initial point before the iteration steps. |
Value
A list consisting of:
G_emtp2 |
The optimal value of the variogram matrix |
it |
The number of iterations |
References
Röttger F, Engelke S, Zwiernik P (2021). “Total positivity in multivariate extremes.” doi:10.48550/ARXIV.2112.14727, https://arxiv.org/abs/2112.14727.
See Also
Other parameter estimation methods:
data2mpareto(),
emp_chi_multdim(),
emp_chi(),
emp_vario(),
fmpareto_HR_MLE(),
fmpareto_graph_HR(),
loglik_HR()